| $O(T^{-1})$ Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games |
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1 |
| $\Lambda$-DARTS: Mitigating Performance Collapse by Harmonizing Operation Selection among Cells |
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5 |
| $\mathcal{O}$-GNN: incorporating ring priors into molecular modeling |
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4 |
| $\mathrm{SE}(3)$-Equivariant Attention Networks for Shape Reconstruction in Function Space |
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2 |
| $\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks |
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5 |
| $\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks |
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7 |
| $k$NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference |
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3 |
| (Certified!!) Adversarial Robustness for Free! |
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❌ |
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6 |
| 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction |
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5 |
| 3D Segmenter: 3D Transformer based Semantic Segmentation via 2D Panoramic Distillation |
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5 |
| 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation |
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5 |
| 3D generation on ImageNet |
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4 |
| A CMDP-within-online framework for Meta-Safe Reinforcement Learning |
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3 |
| A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification |
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5 |
| A Closer Look at Model Adaptation using Feature Distortion and Simplicity Bias |
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4 |
| A Control-Centric Benchmark for Video Prediction |
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5 |
| A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data |
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4 |
| A Differential Geometric View and Explainability of GNN on Evolving Graphs |
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3 |
| A GNN-Guided Predict-and-Search Framework for Mixed-Integer Linear Programming |
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7 |
| A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesis |
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3 |
| A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning |
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2 |
| A General Rank Preserving Framework for Asymmetric Image Retrieval |
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5 |
| A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps |
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5 |
| A Higher Precision Algorithm for Computing the $1$-Wasserstein Distance |
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2 |
| A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond |
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4 |
| A Kernel Perspective of Skip Connections in Convolutional Networks |
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2 |
| A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation |
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3 |
| A Learning Based Hypothesis Test for Harmful Covariate Shift |
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5 |
| A Message Passing Perspective on Learning Dynamics of Contrastive Learning |
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3 |
| A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics |
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5 |
| A Mixture-of-Expert Approach to RL-based Dialogue Management |
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✅ |
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2 |
| A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning |
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5 |
| A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification |
✅ |
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5 |
| A Neural Mean Embedding Approach for Back-door and Front-door Adjustment |
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3 |
| A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks |
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3 |
| A Non-monotonic Self-terminating Language Model |
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3 |
| A Primal-Dual Framework for Transformers and Neural Networks |
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5 |
| A Self-Attention Ansatz for Ab-initio Quantum Chemistry |
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4 |
| A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search |
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6 |
| A Simple Yet Powerful Deep Active Learning With Snapshots Ensembles |
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5 |
| A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks |
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3 |
| A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy |
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4 |
| A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation |
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5 |
| A Theoretical Framework for Inference and Learning in Predictive Coding Networks |
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3 |
| A Theoretical Understanding of Shallow Vision Transformers: Learning, Generalization, and Sample Complexity |
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4 |
| A Theory of Dynamic Benchmarks |
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3 |
| A Time Series is Worth 64 Words: Long-term Forecasting with Transformers |
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3 |
| A Unified Algebraic Perspective on Lipschitz Neural Networks |
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4 |
| A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games |
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4 |
| A Unified Framework for Soft Threshold Pruning |
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4 |
| A VAE for Transformers with Nonparametric Variational Information Bottleneck |
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5 |
| A View From Somewhere: Human-Centric Face Representations |
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5 |
| A critical look at the evaluation of GNNs under heterophily: Are we really making progress? |
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4 |
| A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet |
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6 |
| A law of adversarial risk, interpolation, and label noise |
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2 |
| A new characterization of the edge of stability based on a sharpness measure aware of batch gradient distribution |
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3 |
| A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation |
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4 |
| A theoretical study of inductive biases in contrastive learning |
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2 |
| A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta. |
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4 |
| AANG : Automating Auxiliary Learning |
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6 |
| ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks |
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6 |
| AE-FLOW: Autoencoders with Normalizing Flows for Medical Images Anomaly Detection |
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3 |
| AGRO: Adversarial discovery of error-prone Groups for Robust Optimization |
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5 |
| AIM: Adapting Image Models for Efficient Video Action Recognition |
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6 |
| Accelerated Single-Call Methods for Constrained Min-Max Optimization |
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4 |
| Accelerating Guided Diffusion Sampling with Splitting Numerical Methods |
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4 |
| Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time |
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4 |
| Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference |
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4 |
| Accurate Image Restoration with Attention Retractable Transformer |
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4 |
| Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats |
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3 |
| Achieve the Minimum Width of Neural Networks for Universal Approximation |
❌ |
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0 |
| Achieving Near-Optimal Individual Regret & Low Communications in Multi-Agent Bandits |
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3 |
| Achieving Sub-linear Regret in Infinite Horizon Average Reward Constrained MDP with Linear Function Approximation |
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2 |
| Actionable Neural Representations: Grid Cells from Minimal Constraints |
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2 |
| Active Image Indexing |
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4 |
| Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation |
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7 |
| Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principle |
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5 |
| Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning |
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6 |
| Adaptive Optimization in the $\infty$-Width Limit |
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2 |
| Adaptive Robust Evidential Optimization For Open Set Detection from Imbalanced Data |
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5 |
| Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation |
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7 |
| Advancing Radiograph Representation Learning with Masked Record Modeling |
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6 |
| Adversarial Attacks on Adversarial Bandits |
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1 |
| Adversarial Diversity in Hanabi |
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4 |
| Adversarial Imitation Learning with Preferences |
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3 |
| Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks |
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5 |
| Agent-based Graph Neural Networks |
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4 |
| Agnostic Learning of General ReLU Activation Using Gradient Descent |
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1 |
| Agree to Disagree: Diversity through Disagreement for Better Transferability |
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6 |
| Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness |
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4 |
| Almost Linear Constant-Factor Sketching for $\ell_1$ and Logistic Regression |
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4 |
| Alternating Differentiation for Optimization Layers |
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5 |
| Amortised Invariance Learning for Contrastive Self-Supervision |
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5 |
| An Adaptive Policy to Employ Sharpness-Aware Minimization |
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4 |
| An Additive Instance-Wise Approach to Multi-class Model Interpretation |
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4 |
| An Equal-Size Hard EM Algorithm for Diverse Dialogue Generation |
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5 |
| An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network |
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1 |
| An Extensible Multi-modal Multi-task Object Dataset with Materials |
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❌ |
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4 |
| An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion |
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4 |
| An efficient encoder-decoder architecture with top-down attention for speech separation |
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5 |
| Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning |
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4 |
| Analogy-Forming Transformers for Few-Shot 3D Parsing |
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6 |
| Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel |
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5 |
| Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections |
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✅ |
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3 |
| Anisotropic Message Passing: Graph Neural Networks with Directional and Long-Range Interactions |
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6 |
| Anti-Symmetric DGN: a stable architecture for Deep Graph Networks |
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5 |
| Any-scale Balanced Samplers for Discrete Space |
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5 |
| AnyDA: Anytime Domain Adaptation |
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6 |
| Approximate Bayesian Inference with Stein Functional Variational Gradient Descent |
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5 |
| Approximate Nearest Neighbor Search through Modern Error-Correcting Codes |
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4 |
| Approximate Vanishing Ideal Computations at Scale |
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6 |
| Approximation and non-parametric estimation of functions over high-dimensional spheres via deep ReLU networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations |
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3 |
| Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing |
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4 |
| Are More Layers Beneficial to Graph Transformers? |
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3 |
| Artificial Neuronal Ensembles with Learned Context Dependent Gating |
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3 |
| Ask Me Anything: A simple strategy for prompting language models |
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5 |
| Associative Memory Augmented Asynchronous Spatiotemporal Representation Learning for Event-based Perception |
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5 |
| Asymptotic Instance-Optimal Algorithms for Interactive Decision Making |
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1 |
| Asynchronous Distributed Bilevel Optimization |
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4 |
| Asynchronous Gradient Play in Zero-Sum Multi-agent Games |
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2 |
| AudioGen: Textually Guided Audio Generation |
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3 |
| Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps |
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6 |
| Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation |
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❌ |
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6 |
| Auto-Encoding Goodness of Fit |
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5 |
| AutoGT: Automated Graph Transformer Architecture Search |
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5 |
| AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks |
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6 |
| Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning? |
❌ |
✅ |
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❌ |
❌ |
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4 |
| Automated Data Augmentations for Graph Classification |
✅ |
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5 |
| Automatic Chain of Thought Prompting in Large Language Models |
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4 |
| Automating Nearest Neighbor Search Configuration with Constrained Optimization |
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5 |
| Autoregressive Conditional Neural Processes |
✅ |
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✅ |
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❌ |
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6 |
| Average Sensitivity of Decision Tree Learning |
✅ |
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4 |
| Avoiding spurious correlations via logit correction |
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5 |
| BALTO: fast tensor program optimization with diversity-based active learning |
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4 |
| BAYES RISK CTC: CONTROLLABLE CTC ALIGNMENT IN SEQUENCE-TO-SEQUENCE TASKS |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| BC-IRL: Learning Generalizable Reward Functions from Demonstrations |
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✅ |
❌ |
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3 |
| BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BSTT: A Bayesian Spatial-Temporal Transformer for Sleep Staging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Backpropagation through Combinatorial Algorithms: Identity with Projection Works |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Backstepping Temporal Difference Learning |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Bag of Tricks for Unsupervised Text-to-Speech |
✅ |
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✅ |
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❌ |
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5 |
| Basic Binary Convolution Unit for Binarized Image Restoration Network |
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✅ |
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❌ |
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4 |
| Batch Multivalid Conformal Prediction |
✅ |
✅ |
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✅ |
❌ |
❌ |
✅ |
5 |
| Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images |
❌ |
✅ |
✅ |
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❌ |
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4 |
| Bayesian Oracle for bounding information gain in neural encoding models |
❌ |
❌ |
✅ |
✅ |
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3 |
| Become a Proficient Player with Limited Data through Watching Pure Videos |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Behavior Prior Representation learning for Offline Reinforcement Learning |
✅ |
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❌ |
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4 |
| Behavior Proximal Policy Optimization |
✅ |
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❌ |
❌ |
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4 |
| Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Benchmarking Constraint Inference in Inverse Reinforcement Learning |
✅ |
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✅ |
❌ |
✅ |
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5 |
| Benchmarking Offline Reinforcement Learning on Real-Robot Hardware |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models |
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✅ |
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3 |
| Better Generative Replay for Continual Federated Learning |
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✅ |
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❌ |
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3 |
| Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation |
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❌ |
✅ |
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4 |
| Betty: An Automatic Differentiation Library for Multilevel Optimization |
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✅ |
❌ |
❌ |
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5 |
| Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD |
❌ |
❌ |
❌ |
❌ |
❌ |
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❌ |
0 |
| Beyond calibration: estimating the grouping loss of modern neural networks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bias Propagation in Federated Learning |
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✅ |
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5 |
| Bidirectional Language Models Are Also Few-shot Learners |
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3 |
| BigVGAN: A Universal Neural Vocoder with Large-Scale Training |
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5 |
| Binding Language Models in Symbolic Languages |
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3 |
| Bispectral Neural Networks |
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5 |
| Bit-Pruning: A Sparse Multiplication-Less Dot-Product |
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3 |
| Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts |
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✅ |
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4 |
| Block and Subword-Scaling Floating-Point (BSFP) : An Efficient Non-Uniform Quantization For Low Precision Inference |
✅ |
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4 |
| Blurring Diffusion Models |
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3 |
| Boosting Adversarial Transferability using Dynamic Cues |
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5 |
| Boosting Causal Discovery via Adaptive Sample Reweighting |
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5 |
| Boosting Multiagent Reinforcement Learning via Permutation Invariant and Permutation Equivariant Networks |
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5 |
| Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs |
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4 |
| Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint |
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5 |
| Brain-like representational straightening of natural movies in robust feedforward neural networks |
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2 |
| BrainBERT: Self-supervised representation learning for intracranial recordings |
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4 |
| Breaking Correlation Shift via Conditional Invariant Regularizer |
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4 |
| Bridge the Inference Gaps of Neural Processes via Expectation Maximization |
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5 |
| Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes |
✅ |
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✅ |
❌ |
❌ |
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5 |
| Bridging the Gap to Real-World Object-Centric Learning |
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6 |
| Broken Neural Scaling Laws |
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4 |
| Budgeted Training for Vision Transformer |
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3 |
| Building Normalizing Flows with Stochastic Interpolants |
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3 |
| Building a Subspace of Policies for Scalable Continual Learning |
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5 |
| CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning |
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7 |
| CASR: Generating Complex Sequences with Autoregressive Self-Boost Refinement |
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6 |
| CFlowNets: Continuous Control with Generative Flow Networks |
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4 |
| CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning |
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6 |
| CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks |
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6 |
| CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Alignment |
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5 |
| CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos |
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3 |
| CO3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving |
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❌ |
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3 |
| CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations |
✅ |
✅ |
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❌ |
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4 |
| CUDA: Curriculum of Data Augmentation for Long-tailed Recognition |
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4 |
| CUTS: Neural Causal Discovery from Irregular Time-Series Data |
✅ |
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5 |
| Calibrating Sequence likelihood Improves Conditional Language Generation |
✅ |
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✅ |
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4 |
| Calibrating Transformers via Sparse Gaussian Processes |
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5 |
| Calibrating the Rigged Lottery: Making All Tickets Reliable |
✅ |
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4 |
| Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems |
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5 |
| Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories |
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4 |
| Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study |
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3 |
| Can CNNs Be More Robust Than Transformers? |
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4 |
| Can Neural Networks Learn Implicit Logic from Physical Reasoning? |
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0 |
| Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? |
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4 |
| Can We Find Nash Equilibria at a Linear Rate in Markov Games? |
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2 |
| Can discrete information extraction prompts generalize across language models? |
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3 |
| Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries |
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5 |
| Capturing the Motion of Every Joint: 3D Human Pose and Shape Estimation with Independent Tokens |
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4 |
| Causal Balancing for Domain Generalization |
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6 |
| Causal Confusion and Reward Misidentification in Preference-Based Reward Learning |
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4 |
| Causal Estimation for Text Data with (Apparent) Overlap Violations |
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3 |
| Causal Imitation Learning via Inverse Reinforcement Learning |
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3 |
| Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning |
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3 |
| Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems |
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6 |
| Causality Compensated Attention for Contextual Biased Visual Recognition |
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3 |
| Certifiably Robust Policy Learning against Adversarial Multi-Agent Communication |
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5 |
| Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation |
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5 |
| Certified Training: Small Boxes are All You Need |
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2 |
| Characteristic Neural Ordinary Differential Equation |
✅ |
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5 |
| Characterizing intrinsic compositionality in transformers with Tree Projections |
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4 |
| Characterizing the Influence of Graph Elements |
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3 |
| Characterizing the spectrum of the NTK via a power series expansion |
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❌ |
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❌ |
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3 |
| Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization |
❌ |
✅ |
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✅ |
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4 |
| Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning |
✅ |
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❌ |
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4 |
| ChiroDiff: Modelling chirographic data with Diffusion Models |
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4 |
| ChordMixer: A Scalable Neural Attention Model for Sequences with Different Length |
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5 |
| Choreographer: Learning and Adapting Skills in Imagination |
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6 |
| CircNet: Meshing 3D Point Clouds with Circumcenter Detection |
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3 |
| CktGNN: Circuit Graph Neural Network for Electronic Design Automation |
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4 |
| Classically Approximating Variational Quantum Machine Learning with Random Fourier Features |
✅ |
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❌ |
❌ |
❌ |
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4 |
| Clean-image Backdoor: Attacking Multi-label Models with Poisoned Labels Only |
✅ |
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❌ |
❌ |
❌ |
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2 |
| Clifford Neural Layers for PDE Modeling |
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4 |
| CoRTX: Contrastive Framework for Real-time Explanation |
✅ |
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❌ |
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6 |
| Code Translation with Compiler Representations |
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❌ |
✅ |
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3 |
| CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code |
❌ |
❌ |
✅ |
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4 |
| CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis |
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4 |
| CodeT: Code Generation with Generated Tests |
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✅ |
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❌ |
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3 |
| CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers |
❌ |
✅ |
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❌ |
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4 |
| Collaborative Pure Exploration in Kernel Bandit |
✅ |
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❌ |
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3 |
| Combating Exacerbated Heterogeneity for Robust Models in Federated Learning |
✅ |
✅ |
✅ |
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5 |
| Combinatorial Pure Exploration of Causal Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combinatorial-Probabilistic Trade-Off: P-Values of Community Properties Test in the Stochastic Block Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Competitive Physics Informed Networks |
❌ |
✅ |
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✅ |
✅ |
❌ |
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5 |
| Complexity-Based Prompting for Multi-step Reasoning |
❌ |
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❌ |
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4 |
| Composing Ensembles of Pre-trained Models via Iterative Consensus |
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3 |
| Composing Task Knowledge With Modular Successor Feature Approximators |
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✅ |
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❌ |
❌ |
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2 |
| Composite Slice Transformer: An Efficient Transformer with Composition of Multi-Scale Multi-Range Attentions |
❌ |
❌ |
✅ |
✅ |
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4 |
| Compositional Law Parsing with Latent Random Functions |
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5 |
| Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection |
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4 |
| Compositional Semantic Parsing with Large Language Models |
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3 |
| Compositional Task Representations for Large Language Models |
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4 |
| Compositionality with Variation Reliably Emerges in Neural Networks |
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4 |
| Compressing multidimensional weather and climate data into neural networks |
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4 |
| Computational Language Acquisition with Theory of Mind |
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4 |
| Computing all Optimal Partial Transports |
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4 |
| Concept Gradient: Concept-based Interpretation Without Linear Assumption |
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4 |
| Concept-level Debugging of Part-Prototype Networks |
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5 |
| Conditional Antibody Design as 3D Equivariant Graph Translation |
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5 |
| Conditional Positional Encodings for Vision Transformers |
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6 |
| Confidence Estimation Using Unlabeled Data |
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4 |
| Confidence-Based Feature Imputation for Graphs with Partially Known Features |
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7 |
| Confidence-Conditioned Value Functions for Offline Reinforcement Learning |
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3 |
| Confidential-PROFITT: Confidential PROof of FaIr Training of Trees |
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❌ |
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6 |
| Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization |
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4 |
| Consolidator: Mergable Adapter with Group Connections for Visual Adaptation |
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4 |
| Constraining Representations Yields Models That Know What They Don't Know |
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4 |
| Constructive TT-representation of the tensors given as index interaction functions with applications |
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2 |
| Context-enriched molecule representations improve few-shot drug discovery |
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5 |
| Contextual Convolutional Networks |
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5 |
| Contextual Image Masking Modeling via Synergized Contrasting without View Augmentation for Faster and Better Visual Pretraining |
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5 |
| Contextual bandits with concave rewards, and an application to fair ranking |
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3 |
| Continual Pre-training of Language Models |
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✅ |
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3 |
| Continual Transformers: Redundancy-Free Attention for Online Inference |
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5 |
| Continual Unsupervised Disentangling of Self-Organizing Representations |
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3 |
| Continual evaluation for lifelong learning: Identifying the stability gap |
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4 |
| Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization |
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2 |
| Continuous PDE Dynamics Forecasting with Implicit Neural Representations |
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5 |
| Continuous pseudo-labeling from the start |
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5 |
| Continuous-Discrete Convolution for Geometry-Sequence Modeling in Proteins |
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6 |
| Continuous-time identification of dynamic state-space models by deep subspace encoding |
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4 |
| ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond |
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5 |
| Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning |
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5 |
| Contrastive Audio-Visual Masked Autoencoder |
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5 |
| Contrastive Corpus Attribution for Explaining Representations |
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4 |
| Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions |
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3 |
| Contrastive Learning for Unsupervised Domain Adaptation of Time Series |
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5 |
| Contrastive Meta-Learning for Partially Observable Few-Shot Learning |
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6 |
| Copy is All You Need |
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6 |
| Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation |
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✅ |
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4 |
| Corrupted Image Modeling for Self-Supervised Visual Pre-Training |
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❌ |
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4 |
| Coupled Multiwavelet Operator Learning for Coupled Differential Equations |
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4 |
| Coverage-centric Coreset Selection for High Pruning Rates |
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6 |
| CrAM: A Compression-Aware Minimizer |
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7 |
| Critic Sequential Monte Carlo |
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6 |
| Cross-Layer Retrospective Retrieving via Layer Attention |
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6 |
| Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification |
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3 |
| Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting |
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5 |
| Curriculum-based Co-design of Morphology and Control of Voxel-based Soft Robots |
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4 |
| Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation |
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4 |
| Cycle-consistent Masked AutoEncoder for Unsupervised Domain Generalization |
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3 |
| D4AM: A General Denoising Framework for Downstream Acoustic Models |
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5 |
| D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory |
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3 |
| DAG Learning on the Permutahedron |
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5 |
| DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks |
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4 |
| DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity |
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6 |
| DAVA: Disentangling Adversarial Variational Autoencoder |
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4 |
| DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection |
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4 |
| DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability |
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4 |
| DDM$^2$: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models |
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4 |
| DELTA: DEGRADATION-FREE FULLY TEST-TIME ADAPTATION |
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4 |
| DENSE RGB SLAM WITH NEURAL IMPLICIT MAPS |
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4 |
| DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems |
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5 |
| DFPC: Data flow driven pruning of coupled channels without data. |
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6 |
| DFlow: Learning to Synthesize Better Optical Flow Datasets via a Differentiable Pipeline |
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5 |
| DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion |
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6 |
| DINO as a von Mises-Fisher mixture model |
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4 |
| DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection |
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4 |
| DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images |
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4 |
| DamoFD: Digging into Backbone Design on Face Detection |
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6 |
| Data Continuity Matters: Improving Sequence Modeling with Lipschitz Regularizer |
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4 |
| Data Valuation Without Training of a Model |
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5 |
| Data augmentation alone can improve adversarial training |
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7 |
| Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity |
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✅ |
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4 |
| Dataless Knowledge Fusion by Merging Weights of Language Models |
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5 |
| Dataset Pruning: Reducing Training Data by Examining Generalization Influence |
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4 |
| DaxBench: Benchmarking Deformable Object Manipulation with Differentiable Physics |
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3 |
| De Novo Molecular Generation via Connection-aware Motif Mining |
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5 |
| DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing |
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4 |
| DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training |
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5 |
| DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases |
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4 |
| Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games |
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1 |
| Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models |
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4 |
| Decision S4: Efficient Sequence-Based RL via State Spaces Layers |
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4 |
| Decision Transformer under Random Frame Dropping |
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3 |
| Decompose to Generalize: Species-Generalized Animal Pose Estimation |
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4 |
| Decomposed Prompting: A Modular Approach for Solving Complex Tasks |
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5 |
| Decompositional Generation Process for Instance-Dependent Partial Label Learning |
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5 |
| Deconstructing Distributions: A Pointwise Framework of Learning |
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2 |
| Decoupled Training for Long-Tailed Classification With Stochastic Representations |
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6 |
| Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths |
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5 |
| Deep Ensembles for Graphs with Higher-order Dependencies |
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5 |
| Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models |
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4 |
| Deep Generative Symbolic Regression |
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6 |
| Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization |
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5 |
| Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? |
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2 |
| Deep Learning on Implicit Neural Representations of Shapes |
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5 |
| Deep Ranking Ensembles for Hyperparameter Optimization |
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5 |
| Deep Reinforcement Learning for Cost-Effective Medical Diagnosis |
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5 |
| Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation |
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3 |
| Deep Variational Implicit Processes |
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3 |
| Defending against Adversarial Audio via Diffusion Model |
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5 |
| Deja Vu: Continual Model Generalization for Unseen Domains |
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3 |
| Delving into Semantic Scale Imbalance |
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5 |
| Denoising Diffusion Error Correction Codes |
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5 |
| Denoising Diffusion Samplers |
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4 |
| Denoising Masked Autoencoders Help Robust Classification |
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4 |
| DensePure: Understanding Diffusion Models for Adversarial Robustness |
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5 |
| Depth Separation with Multilayer Mean-Field Networks |
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2 |
| DepthFL : Depthwise Federated Learning for Heterogeneous Clients |
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3 |
| Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling |
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5 |
| Deterministic training of generative autoencoders using invertible layers |
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6 |
| DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics |
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3 |
| DiGress: Discrete Denoising diffusion for graph generation |
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4 |
| Diagnosing and Rectifying Vision Models using Language |
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4 |
| Dichotomy of Control: Separating What You Can Control from What You Cannot |
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5 |
| DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking |
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7 |
| DiffEdit: Diffusion-based semantic image editing with mask guidance |
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4 |
| DiffMimic: Efficient Motion Mimicking with Differentiable Physics |
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5 |
| Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models |
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6 |
| Differentiable Mathematical Programming for Object-Centric Representation Learning |
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6 |
| Differentially Private $L_2$-Heavy Hitters in the Sliding Window Model |
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❌ |
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❌ |
1 |
| Differentially Private Adaptive Optimization with Delayed Preconditioners |
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4 |
| DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models |
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5 |
| DiffusER: Diffusion via Edit-based Reconstruction |
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4 |
| Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation |
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5 |
| Diffusion Models Already Have A Semantic Latent Space |
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5 |
| Diffusion Models for Causal Discovery via Topological Ordering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Diffusion Posterior Sampling for General Noisy Inverse Problems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Diffusion Probabilistic Fields |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Diffusion-GAN: Training GANs with Diffusion |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Diffusion-based Image Translation using disentangled style and content representation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dilated convolution with learnable spacings |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dirichlet-based Uncertainty Calibration for Active Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Discovering Evolution Strategies via Meta-Black-Box Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Discovering Informative and Robust Positives for Video Domain Adaptation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Discovering Latent Knowledge in Language Models Without Supervision |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Discrete Predictor-Corrector Diffusion Models for Image Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Disentanglement of Correlated Factors via Hausdorff Factorized Support |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Disentanglement with Biological Constraints: A Theory of Functional Cell Types |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Disentangling Learning Representations with Density Estimation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Disentangling the Mechanisms Behind Implicit Regularization in SGD |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Disparate Impact in Differential Privacy from Gradient Misalignment |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Distilling Cognitive Backdoor Patterns within an Image |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distilling Model Failures as Directions in Latent Space |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Distributed Differential Privacy in Multi-Armed Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Distributed Extra-gradient with Optimal Complexity and Communication Guarantees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributional Meta-Gradient Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributionally Robust Post-hoc Classifiers under Prior Shifts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distributionally Robust Recourse Action |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Do We Really Need Complicated Model Architectures For Temporal Networks? |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| DocPrompting: Generating Code by Retrieving the Docs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Does Deep Learning Learn to Abstract? A Systematic Probing Framework |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision? |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Does Zero-Shot Reinforcement Learning Exist? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Domain Generalization via Heckman-type Selection Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Don’t fear the unlabelled: safe semi-supervised learning via debiasing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Don’t forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| DreamFusion: Text-to-3D using 2D Diffusion |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Dual Algorithmic Reasoning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dual Diffusion Implicit Bridges for Image-to-Image Translation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Dual Student Networks for Data-Free Model Stealing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| DySR: Adaptive Super-Resolution via Algorithm and System Co-design |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DynaMS: Dyanmic Margin Selection for Efficient Deep Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| EA-HAS-Bench: Energy-aware Hyperparameter and Architecture Search Benchmark |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| EVA3D: Compositional 3D Human Generation from 2D Image Collections |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| EVC: Towards Real-Time Neural Image Compression with Mask Decay |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Easy Differentially Private Linear Regression |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Editing models with task arithmetic |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Effective Self-supervised Pre-training on Low-compute Networks without Distillation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Effective passive membership inference attacks in federated learning against overparameterized models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Effectively Modeling Time Series with Simple Discrete State Spaces |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Effects of Graph Convolutions in Multi-layer Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient Attention via Control Variates |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Certified Training and Robustness Verification of Neural ODEs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Efficient Conditionally Invariant Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Deep Reinforcement Learning Requires Regulating Overfitting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Discrete Multi Marginal Optimal Transport Regularization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Edge Inference by Selective Query |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Federated Domain Translation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Model Updates for Approximate Unlearning of Graph-Structured Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Offline Policy Optimization with a Learned Model |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Planning in a Compact Latent Action Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient approximation of neural population structure and correlations with probabilistic circuits |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient recurrent architectures through activity sparsity and sparse back-propagation through time |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficiently Computing Nash Equilibria in Adversarial Team Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficiently Controlling Multiple Risks with Pareto Testing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Emergence of Maps in the Memories of Blind Navigation Agents |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Empowering Graph Representation Learning with Test-Time Graph Transformation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Encoding Recurrence into Transformers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Energy-Based Test Sample Adaptation for Domain Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Energy-Inspired Self-Supervised Pretraining for Vision Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Energy-based Out-of-Distribution Detection for Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Enhancing Meta Learning via Multi-Objective Soft Improvement Functions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Equal Improvability: A New Fairness Notion Considering the Long-term Impact |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| EquiMod: An Equivariance Module to Improve Visual Instance Discrimination |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Equivariance-aware Architectural Optimization of Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Equivariant Energy-Guided SDE for Inverse Molecular Design |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Equivariant Hypergraph Diffusion Neural Operators |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Estimating individual treatment effects under unobserved confounding using binary instruments |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Eva: Practical Second-order Optimization with Kronecker-vectorized Approximation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Evaluating Long-Term Memory in 3D Mazes |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Evaluating Representations with Readout Model Switching |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Everybody Needs Good Neighbours: An Unsupervised Locality-based Method for Bias Mitigation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Evidential Uncertainty and Diversity Guided Active Learning for Scene Graph Generation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Evolving Populations of Diverse RL Agents with MAP-Elites |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Explaining RL Decisions with Trajectories |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Explaining Temporal Graph Models through an Explorer-Navigator Framework |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Explicitly Minimizing the Blur Error of Variational Autoencoders |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploring Active 3D Object Detection from a Generalization Perspective |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploring Low-Rank Property in Multiple Instance Learning for Whole Slide Image Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring Temporally Dynamic Data Augmentation for Video Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring perceptual straightness in learned visual representations |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exponential Generalization Bounds with Near-Optimal Rates for $L_q$-Stable Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Expressive Monotonic Neural Networks |
❌ |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Extracting Robust Models with Uncertain Examples |
✅ |
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✅ |
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❌ |
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6 |
| Extreme Q-Learning: MaxEnt RL without Entropy |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Extremely Simple Activation Shaping for Out-of-Distribution Detection |
✅ |
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✅ |
✅ |
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❌ |
✅ |
6 |
| FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FIGARO: Controllable Music Generation using Learned and Expert Features |
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❌ |
✅ |
6 |
| FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities |
❌ |
✅ |
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✅ |
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✅ |
5 |
| FIT: A Metric for Model Sensitivity |
❌ |
✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Factorized Fourier Neural Operators |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FaiREE: fair classification with finite-sample and distribution-free guarantee |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fair Attribute Completion on Graph with Missing Attributes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FairGBM: Gradient Boosting with Fairness Constraints |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fairness and Accuracy under Domain Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fast Nonlinear Vector Quantile Regression |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Sampling of Diffusion Models with Exponential Integrator |
✅ |
✅ |
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❌ |
❌ |
❌ |
✅ |
4 |
| Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search |
✅ |
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✅ |
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❌ |
✅ |
6 |
| FastFill: Efficient Compatible Model Update |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Faster Gradient-Free Methods for Escaping Saddle Points |
✅ |
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❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Faster federated optimization under second-order similarity |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Feature selection and low test error in shallow low-rotation ReLU networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| FedDAR: Federated Domain-Aware Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
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✅ |
5 |
| FedExP: Speeding Up Federated Averaging via Extrapolation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FedFA: Federated Feature Augmentation |
✅ |
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✅ |
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❌ |
✅ |
6 |
| FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy |
✅ |
❌ |
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❌ |
✅ |
❌ |
✅ |
4 |
| Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Federated Learning from Small Datasets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Federated Nearest Neighbor Machine Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Federated Neural Bandits |
✅ |
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❌ |
✅ |
❌ |
✅ |
5 |
| Few-Shot Domain Adaptation For End-to-End Communication |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Few-shot Backdoor Attacks via Neural Tangent Kernels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Filter-Recovery Network for Multi-Speaker Audio-Visual Speech Separation |
❌ |
❌ |
✅ |
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❌ |
❌ |
✅ |
3 |
| Finding Actual Descent Directions for Adversarial Training |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Finding the Global Semantic Representation in GAN through Fréchet Mean |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fisher-Legendre (FishLeg) optimization of deep neural networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Flow Annealed Importance Sampling Bootstrap |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Flow Matching for Generative Modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| FoSR: First-order spectral rewiring for addressing oversquashing in GNNs |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Fooling SHAP with Stealthily Biased Sampling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Formal Mathematics Statement Curriculum Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Free Lunch for Domain Adversarial Training: Environment Label Smoothing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning |
✅ |
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✅ |
❌ |
✅ |
❌ |
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5 |
| From $t$-SNE to UMAP with contrastive learning |
✅ |
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7 |
| From Play to Policy: Conditional Behavior Generation from Uncurated Robot Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Function-Consistent Feature Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Function-space regularized Rényi divergences |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fundamental Limits in Formal Verification of Message-Passing Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fundamental limits on the robustness of image classifiers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| FunkNN: Neural Interpolation for Functional Generation |
❌ |
✅ |
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❌ |
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4 |
| Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GAIN: On the Generalization of Instructional Action Understanding |
❌ |
✅ |
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❌ |
❌ |
❌ |
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3 |
| GAMR: A Guided Attention Model for (visual) Reasoning |
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✅ |
✅ |
❌ |
❌ |
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4 |
| GEASS: Neural causal feature selection for high-dimensional biological data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| GFlowNets and variational inference |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| GLM-130B: An Open Bilingual Pre-trained Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| GNNDelete: A General Strategy for Unlearning in Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks |
✅ |
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❌ |
✅ |
✅ |
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5 |
| GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure |
❌ |
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❌ |
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5 |
| GOOD: Exploring geometric cues for detecting objects in an open world |
❌ |
✅ |
✅ |
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❌ |
❌ |
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4 |
| GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation |
❌ |
✅ |
✅ |
❌ |
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4 |
| GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints |
✅ |
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✅ |
❌ |
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❌ |
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5 |
| GReTo: Remedying dynamic graph topology-task discordance via target homophily |
❌ |
✅ |
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❌ |
❌ |
❌ |
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3 |
| GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis |
❌ |
✅ |
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❌ |
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4 |
| General Neural Gauge Fields |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalization and Estimation Error Bounds for Model-based Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
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3 |
| Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap |
❌ |
❌ |
✅ |
✅ |
❌ |
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✅ |
3 |
| Generate rather than Retrieve: Large Language Models are Strong Context Generators |
❌ |
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❌ |
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5 |
| Generating Diverse Cooperative Agents by Learning Incompatible Policies |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generating Sequences by Learning to Self-Correct |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generative Augmented Flow Networks |
✅ |
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❌ |
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❌ |
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4 |
| Generative Modeling Helps Weak Supervision (and Vice Versa) |
✅ |
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❌ |
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6 |
| Generative Modelling with Inverse Heat Dissipation |
✅ |
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✅ |
❌ |
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❌ |
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5 |
| Geometrically regularized autoencoders for non-Euclidean data |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Git Re-Basin: Merging Models modulo Permutation Symmetries |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Global Explainability of GNNs via Logic Combination of Learned Concepts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Globally Optimal Training of Neural Networks with Threshold Activation Functions |
❌ |
❌ |
✅ |
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✅ |
❌ |
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3 |
| GoBigger: A Scalable Platform for Cooperative-Competitive Multi-Agent Interactive Simulation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gradient Boosting Performs Gaussian Process Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Gradient Gating for Deep Multi-Rate Learning on Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Graph Contrastive Learning for Skeleton-based Action Recognition |
❌ |
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❌ |
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5 |
| Graph Domain Adaptation via Theory-Grounded Spectral Regularization |
❌ |
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❌ |
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5 |
| Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs |
❌ |
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6 |
| Graph Neural Networks for Link Prediction with Subgraph Sketching |
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❌ |
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6 |
| Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution |
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✅ |
✅ |
✅ |
7 |
| Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems |
✅ |
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✅ |
❌ |
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❌ |
✅ |
5 |
| Gray-Box Gaussian Processes for Automated Reinforcement Learning |
✅ |
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✅ |
❌ |
❌ |
❌ |
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4 |
| Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gromov-Wasserstein Autoencoders |
❌ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Grounding Graph Network Simulators using Physical Sensor Observations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Guarded Policy Optimization with Imperfect Online Demonstrations |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Guiding Energy-based Models via Contrastive Latent Variables |
✅ |
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5 |
| Guiding Safe Exploration with Weakest Preconditions |
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❌ |
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4 |
| Guiding continuous operator learning through Physics-based boundary constraints |
✅ |
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❌ |
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4 |
| H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Hard-Meta-Dataset++: Towards Understanding Few-Shot Performance on Difficult Tasks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting |
❌ |
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❌ |
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❌ |
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4 |
| Harnessing Out-Of-Distribution Examples via Augmenting Content and Style |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hebbian Deep Learning Without Feedback |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| HiT-MDP: Learning the SMDP option framework on MDPs with Hidden Temporal Embeddings |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| HiViT: A Simpler and More Efficient Design of Hierarchical Vision Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Hidden Markov Transformer for Simultaneous Machine Translation |
✅ |
✅ |
✅ |
✅ |
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❌ |
✅ |
6 |
| Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion |
✅ |
✅ |
✅ |
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6 |
| Hierarchical Sliced Wasserstein Distance |
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4 |
| Holistic Adversarially Robust Pruning |
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5 |
| HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers |
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5 |
| HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing |
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5 |
| How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study |
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✅ |
2 |
| How I Learned to Stop Worrying and Love Retraining |
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4 |
| How Informative is the Approximation Error from Tensor Decomposition for Neural Network Compression? |
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4 |
| How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization |
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❌ |
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5 |
| How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules |
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❌ |
❌ |
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4 |
| How Sharpness-Aware Minimization Minimizes Sharpness? |
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❌ |
❌ |
❌ |
0 |
| How gradient estimator variance and bias impact learning in neural networks |
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3 |
| How robust is unsupervised representation learning to distribution shift? |
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3 |
| How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? |
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6 |
| How to Train your HIPPO: State Space Models with Generalized Orthogonal Basis Projections |
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2 |
| How to prepare your task head for finetuning |
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4 |
| Human Motion Diffusion Model |
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4 |
| Human MotionFormer: Transferring Human Motions with Vision Transformers |
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4 |
| Human alignment of neural network representations |
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4 |
| Human-Guided Fair Classification for Natural Language Processing |
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✅ |
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❌ |
❌ |
✅ |
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4 |
| Human-level Atari 200x faster |
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❌ |
✅ |
4 |
| Humanly Certifying Superhuman Classifiers |
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❌ |
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❌ |
✅ |
4 |
| Hungry Hungry Hippos: Towards Language Modeling with State Space Models |
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❌ |
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6 |
| Hybrid RL: Using both offline and online data can make RL efficient |
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4 |
| HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization |
❌ |
✅ |
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❌ |
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✅ |
4 |
| Hyper-Decision Transformer for Efficient Online Policy Adaptation |
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3 |
| HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork |
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❌ |
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3 |
| Hyperbolic Deep Reinforcement Learning |
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❌ |
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❌ |
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3 |
| Hyperbolic Self-paced Learning for Self-supervised Skeleton-based Action Representations |
❌ |
✅ |
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✅ |
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❌ |
✅ |
5 |
| Hyperparameter Optimization through Neural Network Partitioning |
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6 |
| IDEAL: Query-Efficient Data-Free Learning from Black-Box Models |
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3 |
| ILA-DA: Improving Transferability of Intermediate Level Attack with Data Augmentation |
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❌ |
✅ |
6 |
| IS SYNTHETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION? |
❌ |
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4 |
| ISAAC Newton: Input-based Approximate Curvature for Newton's Method |
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4 |
| ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation |
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❌ |
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4 |
| Identifiability Results for Multimodal Contrastive Learning |
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❌ |
✅ |
3 |
| Image as Set of Points |
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❌ |
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5 |
| Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction |
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4 |
| ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations |
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4 |
| Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules |
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4 |
| ImaginaryNet: Learning Object Detectors without Real Images and Annotations |
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4 |
| Imbalanced Semi-supervised Learning with Bias Adaptive Classifier |
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❌ |
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6 |
| Imitating Graph-Based Planning with Goal-Conditioned Policies |
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✅ |
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4 |
| Imitating Human Behaviour with Diffusion Models |
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❌ |
✅ |
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4 |
| Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions |
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2 |
| Implicit Regularization for Group Sparsity |
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3 |
| Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent |
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2 |
| Impossibly Good Experts and How to Follow Them |
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❌ |
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4 |
| Improved Convergence of Differential Private SGD with Gradient Clipping |
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4 |
| Improved Learning-augmented Algorithms for k-means and k-medians Clustering |
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✅ |
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❌ |
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❌ |
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5 |
| Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography |
❌ |
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4 |
| Improving Deep Policy Gradients with Value Function Search |
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4 |
| Improving Deep Regression with Ordinal Entropy |
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3 |
| Improving Differentiable Neural Architecture Search by Encouraging Transferability |
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6 |
| Improving Object-centric Learning with Query Optimization |
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4 |
| Improving Out-of-distribution Generalization with Indirection Representations |
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3 |
| Improving the imputation of missing data with Markov Blanket discovery |
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4 |
| In-Situ Text-Only Adaptation of Speech Models with Low-Overhead Speech Imputations |
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5 |
| In-context Reinforcement Learning with Algorithm Distillation |
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2 |
| In-sample Actor Critic for Offline Reinforcement Learning |
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4 |
| InCoder: A Generative Model for Code Infilling and Synthesis |
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5 |
| InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning |
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❌ |
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3 |
| Incompatibility Clustering as a Defense Against Backdoor Poisoning Attacks |
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4 |
| Incremental Learning of Structured Memory via Closed-Loop Transcription |
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5 |
| Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning |
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5 |
| Individual Privacy Accounting with Gaussian Differential Privacy |
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4 |
| Inequality phenomenon in $l_{\infty}$-adversarial training, and its unrealized threats |
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3 |
| Information Plane Analysis for Dropout Neural Networks |
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❌ |
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4 |
| Information-Theoretic Analysis of Unsupervised Domain Adaptation |
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5 |
| Information-Theoretic Diffusion |
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5 |
| Instance-wise Batch Label Restoration via Gradients in Federated Learning |
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4 |
| Integrating Symmetry into Differentiable Planning with Steerable Convolutions |
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4 |
| Interaction-Based Disentanglement of Entities for Object-Centric World Models |
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2 |
| Interactive Portrait Harmonization |
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4 |
| Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation |
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2 |
| Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small |
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2 |
| Interpretability with full complexity by constraining feature information |
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5 |
| Interpretable Debiasing of Vectorized Language Representations with Iterative Orthogonalization |
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5 |
| Interpretable Geometric Deep Learning via Learnable Randomness Injection |
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4 |
| Interpretations of Domain Adaptations via Layer Variational Analysis |
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5 |
| Investigating Multi-task Pretraining and Generalization in Reinforcement Learning |
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3 |
| Is Adversarial Training Really a Silver Bullet for Mitigating Data Poisoning? |
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4 |
| Is Attention All That NeRF Needs? |
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4 |
| Is Conditional Generative Modeling all you need for Decision Making? |
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3 |
| Is Forgetting Less a Good Inductive Bias for Forward Transfer? |
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4 |
| Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function |
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3 |
| Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization |
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5 |
| Is a Caption Worth a Thousand Images? A Study on Representation Learning |
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3 |
| Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification |
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5 |
| Iterative Circuit Repair Against Formal Specifications |
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7 |
| Iterative Patch Selection for High-Resolution Image Recognition |
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6 |
| Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks |
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❌ |
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5 |
| Jointly Learning Visual and Auditory Speech Representations from Raw Data |
❌ |
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5 |
| Kernel Neural Optimal Transport |
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4 |
| KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP |
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4 |
| Knowledge Distillation based Degradation Estimation for Blind Super-Resolution |
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4 |
| Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models |
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❌ |
✅ |
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4 |
| Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts |
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❌ |
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4 |
| KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals |
✅ |
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4 |
| LAVA: Data Valuation without Pre-Specified Learning Algorithms |
❌ |
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4 |
| LDMIC: Learning-based Distributed Multi-view Image Coding |
❌ |
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6 |
| LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence |
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❌ |
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6 |
| LMSeg: Language-guided Multi-dataset Segmentation |
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❌ |
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4 |
| LPT: Long-tailed Prompt Tuning for Image Classification |
❌ |
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4 |
| LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning |
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5 |
| Label Propagation with Weak Supervision |
❌ |
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❌ |
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5 |
| Label-free Concept Bottleneck Models |
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❌ |
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5 |
| Language Modelling with Pixels |
✅ |
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✅ |
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❌ |
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6 |
| Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought |
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4 |
| Language Models Can Teach Themselves to Program Better |
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3 |
| Language Models are Realistic Tabular Data Generators |
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4 |
| Language models are multilingual chain-of-thought reasoners |
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3 |
| Large Language Models are Human-Level Prompt Engineers |
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❌ |
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5 |
| Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations |
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✅ |
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5 |
| Latent Bottlenecked Attentive Neural Processes |
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4 |
| Latent Graph Inference using Product Manifolds |
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5 |
| Latent Neural ODEs with Sparse Bayesian Multiple Shooting |
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6 |
| Latent State Marginalization as a Low-cost Approach for Improving Exploration |
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5 |
| Latent Variable Representation for Reinforcement Learning |
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❌ |
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4 |
| Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations |
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5 |
| Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection |
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4 |
| Learnable Graph Convolutional Attention Networks |
❌ |
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5 |
| Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks |
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❌ |
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4 |
| Learned Index with Dynamic $\epsilon$ |
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5 |
| Learning About Progress From Experts |
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4 |
| Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward |
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❌ |
❌ |
❌ |
✅ |
3 |
| Learning Adversarial Linear Mixture Markov Decision Processes with Bandit Feedback and Unknown Transition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Continuous Normalizing Flows For Faster Convergence To Target Distribution via Ascent Regularizations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Controllable Adaptive Simulation for Multi-resolution Physics |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Diffusion Bridges on Constrained Domains |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Domain-Agnostic Representation for Disease Diagnosis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Fair Graph Representations via Automated Data Augmentations |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Fast and Slow for Online Time Series Forecasting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Group Importance using the Differentiable Hypergeometric Distribution |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Harmonic Molecular Representations on Riemannian Manifold |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Hierarchical Protein Representations via Complete 3D Graph Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Human-Compatible Representations for Case-Based Decision Support |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Hyper Label Model for Programmatic Weak Supervision |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Iterative Neural Optimizers for Image Steganography |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Label Encodings for Deep Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Language Representations with Logical Inductive Bias |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Locality and Isotropy in Dialogue Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Multimodal Data Augmentation in Feature Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Object-Language Alignments for Open-Vocabulary Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Probabilistic Topological Representations Using Discrete Morse Theory |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Proximal Operators to Discover Multiple Optima |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Rationalizable Equilibria in Multiplayer Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning ReLU networks to high uniform accuracy is intractable |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Simultaneous Navigation and Construction in Grid Worlds |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Soft Constraints From Constrained Expert Demonstrations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Sparse Group Models Through Boolean Relaxation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Structured Representations by Embedding Class Hierarchy |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Symbolic Models for Graph-structured Physical Mechanism |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Uncertainty for Unknown Domains with Zero-Target-Assumption |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Vortex Dynamics for Fluid Inference and Prediction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning What and Where: Disentangling Location and Identity Tracking Without Supervision |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning differentiable solvers for systems with hard constraints |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning in temporally structured environments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning multi-scale local conditional probability models of images |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning on Large-scale Text-attributed Graphs via Variational Inference |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning rigid dynamics with face interaction graph networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning the Positions in CountSketch |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to CROSS exchange to solve min-max vehicle routing problems |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning to Compose Soft Prompts for Compositional Zero-Shot Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Decompose Visual Features with Latent Textual Prompts |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Estimate Shapley Values with Vision Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning to Extrapolate: A Transductive Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Generate Columns with Application to Vertex Coloring |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Grow Pretrained Models for Efficient Transformer Training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Induce Causal Structure |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Jointly Share and Prune Weights for Grounding Based Vision and Language Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning to Segment from Noisy Annotations: A Spatial Correction Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to reason over visual objects |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning topology-preserving data representations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning where and when to reason in neuro-symbolic inference |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Learning with Auxiliary Activation for Memory-Efficient Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning with Logical Constraints but without Shortcut Satisfaction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning with Stochastic Orders |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Least-to-Most Prompting Enables Complex Reasoning in Large Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Leveraging Importance Weights in Subset Selection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Leveraging Large Language Models for Multiple Choice Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Leveraging Unlabeled Data to Track Memorization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LiftedCL: Lifting Contrastive Learning for Human-Centric Perception |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Light Sampling Field and BRDF Representation for Physically-based Neural Rendering |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Limitless Stability for Graph Convolutional Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Linear Connectivity Reveals Generalization Strategies |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linearly Mapping from Image to Text Space |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Link Prediction with Non-Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LipsFormer: Introducing Lipschitz Continuity to Vision Transformers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Liquid Structural State-Space Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Localized Randomized Smoothing for Collective Robustness Certification |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| LogicDP: Creating Labels for Graph Data via Inductive Logic Programming |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Logical Message Passing Networks with One-hop Inference on Atomic Formulas |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Long Range Language Modeling via Gated State Spaces |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Long-Tailed Learning Requires Feature Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Long-Tailed Partial Label Learning via Dynamic Rebalancing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MA-BERT: Towards Matrix Arithmetic-only BERT Inference by Eliminating Complex Non-Linear Functions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MARS: Meta-learning as Score Matching in the Function Space |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MCAL: Minimum Cost Human-Machine Active Labeling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MECTA: Memory-Economic Continual Test-Time Model Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| MEDFAIR: Benchmarking Fairness for Medical Imaging |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MEDICAL IMAGE UNDERSTANDING WITH PRETRAINED VISION LANGUAGE MODELS: A COMPREHENSIVE STUDY |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MIMT: Masked Image Modeling Transformer for Video Compression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MPCFORMER: FAST, PERFORMANT AND PRIVATE TRANSFORMER INFERENCE WITH MPC |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Machine Unlearning of Federated Clusters |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Make-A-Video: Text-to-Video Generation without Text-Video Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Making Better Decision by Directly Planning in Continuous Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Malign Overfitting: Interpolation and Invariance are Fundamentally at Odds |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ManyDG: Many-domain Generalization for Healthcare Applications |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Markup-to-Image Diffusion Models with Scheduled Sampling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Martingale Posterior Neural Processes |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| MaskViT: Masked Visual Pre-Training for Video Prediction |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Masked Distillation with Receptive Tokens |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Masked Frequency Modeling for Self-Supervised Visual Pre-Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Masked Image Modeling with Denoising Contrast |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Masked Unsupervised Self-training for Label-free Image Classification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Masked Vision and Language Modeling for Multi-modal Representation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Mass-Editing Memory in a Transformer |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Massively Scaling Heteroscedastic Classifiers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Matching receptor to odorant with protein language and graph neural networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Measure the Predictive Heterogeneity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Measuring Forgetting of Memorized Training Examples |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Measuring axiomatic soundness of counterfactual image models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mega: Moving Average Equipped Gated Attention |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Memorization Capacity of Neural Networks with Conditional Computation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Memorization-Dilation: Modeling Neural Collapse Under Noise |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Memory Gym: Partially Observable Challenges to Memory-Based Agents |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MeshDiffusion: Score-based Generative 3D Mesh Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Meta Knowledge Condensation for Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta Temporal Point Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta-Learning in Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mid-Vision Feedback |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Mind the Gap: Offline Policy Optimization for Imperfect Rewards |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mind the Pool: Convolutional Neural Networks Can Overfit Input Size |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mind's Eye: Grounded Language Model Reasoning through Simulation |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Mini-batch $k$-means terminates within $O(d/\epsilon)$ iterations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimax Optimal Kernel Operator Learning via Multilevel Training |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimum Description Length Control |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimum Variance Unbiased N:M Sparsity for the Neural Gradients |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mitigating Dataset Bias by Using Per-Sample Gradient |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mitigating Memorization of Noisy Labels via Regularization between Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MocoSFL: enabling cross-client collaborative self-supervised learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model-based Causal Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Modeling content creator incentives on algorithm-curated platforms |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Molecule Generation For Target Protein Binding with Structural Motifs |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Monocular Scene Reconstruction with 3D SDF Transformers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Mosaic Representation Learning for Self-supervised Visual Pre-training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Multi-Objective Online Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-domain image generation and translation with identifiability guarantees |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-level Protein Structure Pre-training via Prompt Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-lingual Evaluation of Code Generation Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-objective optimization via equivariant deep hypervolume approximation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-skill Mobile Manipulation for Object Rearrangement |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MultiViz: Towards Visualizing and Understanding Multimodal Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multifactor Sequential Disentanglement via Structured Koopman Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Multimodal Analogical Reasoning over Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multimodal Federated Learning via Contrastive Representation Ensemble |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multiple sequence alignment as a sequence-to-sequence learning problem |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multivariate Time-series Imputation with Disentangled Temporal Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Mutual Partial Label Learning with Competitive Label Noise |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| NERDS: A General Framework to Train Camera Denoisers from Raw-RGB Noisy Image Pairs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| NORM: Knowledge Distillation via N-to-One Representation Matching |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| NTK-SAP: Improving neural network pruning by aligning training dynamics |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| NeRN: Learning Neural Representations for Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Near-Optimal Adversarial Reinforcement Learning with Switching Costs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-optimal Coresets for Robust Clustering |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Near-optimal Policy Identification in Active Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Agents Struggle to Take Turns in Bidirectional Emergent Communication |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Architecture Design and Robustness: A Dataset |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Bregman Divergences for Distance Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Causal Models for Counterfactual Identification and Estimation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Compositional Rule Learning for Knowledge Graph Reasoning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural DAG Scheduling via One-Shot Priority Sampling |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Design for Genetic Perturbation Experiments |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural Episodic Control with State Abstraction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Groundplans: Persistent Neural Scene Representations from a Single Image |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Image-based Avatars: Generalizable Radiance Fields for Human Avatar Modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Neural Implicit Shape Editing using Boundary Sensitivity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Lagrangian Schr\"{o}dinger Bridge: Diffusion Modeling for Population Dynamics |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Networks Efficiently Learn Low-Dimensional Representations with SGD |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neural Networks and the Chomsky Hierarchy |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Radiance Field Codebooks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Systematic Binder |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural ePDOs: Spatially Adaptive Equivariant Partial Differential Operator Based Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Neural-based classification rule learning for sequential data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neuro-Symbolic Procedural Planning with Commonsense Prompting |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| New Insights for the Stability-Plasticity Dilemma in Online Continual Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| No Reason for No Supervision: Improved Generalization in Supervised Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Noise Injection Node Regularization for Robust Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Noise-Robust De-Duplication at Scale |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Non-parametric Outlier Synthesis |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Novel View Synthesis with Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| OPTQ: Accurate Quantization for Generative Pre-trained Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| OTOv2: Automatic, Generic, User-Friendly |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Offline Congestion Games: How Feedback Type Affects Data Coverage Requirement |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Offline RL for Natural Language Generation with Implicit Language Q Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Ollivier-Ricci Curvature for Hypergraphs: A Unified Framework |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Omnigrok: Grokking Beyond Algorithmic Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Accelerated Perceptrons and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Achieving Optimal Adversarial Test Error |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Compositional Uncertainty Quantification for Seq2seq Graph Parsing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| On Explaining Neural Network Robustness with Activation Path |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Pre-training Language Model for Antibody |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Representing Linear Programs by Graph Neural Networks |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Representing Mixed-Integer Linear Programs by Graph Neural Networks |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| On The Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| On The Relative Error of Random Fourier Features for Preserving Kernel Distance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On The Specialization of Neural Modules |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On amortizing convex conjugates for optimal transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Convergence of AdaGrad(Norm) on $\mathbb{R}^d$: Beyond Convexity, Non-Asymptotic Rate and Acceleration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Data-Efficiency with Contrastive Image Transformation in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning. |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Importance and Applicability of Pre-Training for Federated Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Performance of Temporal Difference Learning With Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Perils of Cascading Robust Classifiers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Robustness of Safe Reinforcement Learning under Observational Perturbations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Saturation Effect of Kernel Ridge Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Sensitivity of Reward Inference to Misspecified Human Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Soft-Subnetwork for Few-Shot Class Incremental Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| On the Trade-Off between Actionable Explanations and the Right to be Forgotten |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Usefulness of Embeddings, Clusters and Strings for Text Generation Evaluation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Word Boundaries of Emergent Languages Based on Harris's Articulation Scheme |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the complexity of nonsmooth automatic differentiation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the duality between contrastive and non-contrastive self-supervised learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| One Transformer Can Understand Both 2D & 3D Molecular Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| One-Pixel Shortcut: On the Learning Preference of Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Online Bias Correction for Task-Free Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Online Boundary-Free Continual Learning by Scheduled Data Prior |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online Low Rank Matrix Completion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Open-Vocabulary Object Detection upon Frozen Vision and Language Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Optimal Activation Functions for the Random Features Regression Model |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Transport for Offline Imitation Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimizing Spca-based Continual Learning: A Theoretical Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Order Matters: Agent-by-agent Policy Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Out-of-Distribution Detection and Selective Generation for Conditional Language Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Out-of-distribution Detection with Implicit Outlier Transformation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Out-of-distribution Representation Learning for Time Series Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Outcome-directed Reinforcement Learning by Uncertainty \& Temporal Distance-Aware Curriculum Goal Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Over-Training with Mixup May Hurt Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Over-parameterized Model Optimization with Polyak-{\L}ojasiewicz Condition |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PAC Reinforcement Learning for Predictive State Representations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| PASHA: Efficient HPO and NAS with Progressive Resource Allocation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PEER: A Collaborative Language Model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PGrad: Learning Principal Gradients For Domain Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PLOT: Prompt Learning with Optimal Transport for Vision-Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| POPGym: Benchmarking Partially Observable Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PV3D: A 3D Generative Model for Portrait Video Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PaLI: A Jointly-Scaled Multilingual Language-Image Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Packed Ensembles for efficient uncertainty estimation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Parallel Deep Neural Networks Have Zero Duality Gap |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Parameter-Efficient Fine-Tuning Design Spaces |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Parametrizing Product Shape Manifolds by Composite Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Part-Based Models Improve Adversarial Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Particle-based Variational Inference with Preconditioned Functional Gradient Flow |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PatchDCT: Patch Refinement for High Quality Instance Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PerFedMask: Personalized Federated Learning with Optimized Masking Vectors |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Perfectly Secure Steganography Using Minimum Entropy Coupling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Personalized Federated Learning with Feature Alignment and Classifier Collaboration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Personalized Reward Learning with Interaction-Grounded Learning (IGL) |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Phase transition for detecting a small community in a large network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Phase2vec: dynamical systems embedding with a physics-informed convolutional network |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Phenaki: Variable Length Video Generation from Open Domain Textual Descriptions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PiFold: Toward effective and efficient protein inverse folding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Pitfalls of Gaussians as a noise distribution in NCE |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Planning Goals for Exploration |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Planning with Large Language Models for Code Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Planning with Sequence Models through Iterative Energy Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Plateau in Monotonic Linear Interpolation --- A "Biased" View of Loss Landscape for Deep Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Policy Expansion for Bridging Offline-to-Online Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Policy-Based Self-Competition for Planning Problems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Population-size-Aware Policy Optimization for Mean-Field Games |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Post-hoc Concept Bottleneck Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Powderworld: A Platform for Understanding Generalization via Rich Task Distributions |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| PowerQuant: Automorphism Search for Non-Uniform Quantization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Pre-training via Denoising for Molecular Property Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Predictive Inference with Feature Conformal Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Predictor-corrector algorithms for stochastic optimization under gradual distribution shift |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Preference Transformer: Modeling Human Preferences using Transformers for RL |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Proactive Multi-Camera Collaboration for 3D Human Pose Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Programmatically Grounded, Compositionally Generalizable Robotic Manipulation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Progress measures for grokking via mechanistic interpretability |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Progressive Prompts: Continual Learning for Language Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Progressively Compressed Auto-Encoder for Self-supervised Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Projective Proximal Gradient Descent for Nonconvex Nonsmooth Optimization: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Prompt-to-Prompt Image Editing with Cross-Attention Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Promptagator: Few-shot Dense Retrieval From 8 Examples |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Prompting GPT-3 To Be Reliable |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Proposal-Contrastive Pretraining for Object Detection from Fewer Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Protein Representation Learning by Geometric Structure Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Protein Representation Learning via Knowledge Enhanced Primary Structure Reasoning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Protein Sequence and Structure Co-Design with Equivariant Translation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Prototypical Calibration for Few-shot Learning of Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Provable Defense Against Geometric Transformations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Provable Memorization Capacity of Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Provable Robustness against Wasserstein Distribution Shifts via Input Randomization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Provable Sim-to-real Transfer in Continuous Domain with Partial Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Auditing Ordinary Least Squares in Low Dimensions |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Provably Efficient Lifelong Reinforcement Learning with Linear Representation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Efficient Risk-Sensitive Reinforcement Learning: Iterated CVaR and Worst Path |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pruning Deep Neural Networks from a Sparsity Perspective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pseudo-label Training and Model Inertia in Neural Machine Translation |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Pseudoinverse-Guided Diffusion Models for Inverse Problems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| QAID: Question Answering Inspired Few-shot Intent Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| QuAnt: Quantum Annealing with Learnt Couplings |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Quality-Similar Diversity via Population Based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Quantifying Memorization Across Neural Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Quantile Risk Control: A Flexible Framework for Bounding the Probability of High-Loss Predictions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Quantized Compressed Sensing with Score-Based Generative Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Quasi-optimal Reinforcement Learning with Continuous Actions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| REPAIR: REnormalizing Permuted Activations for Interpolation Repair |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RPM: Generalizable Multi-Agent Policies for Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Random Laplacian Features for Learning with Hyperbolic Space |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Re-Imagen: Retrieval-Augmented Text-to-Image Generator |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Re-calibrating Feature Attributions for Model Interpretation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Re-parameterizing Your Optimizers rather than Architectures |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ReAct: Synergizing Reasoning and Acting in Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Real-Time Image Demoir$\acute{e}$ing on Mobile Devices |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Real-time variational method for learning neural trajectory and its dynamics |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Recitation-Augmented Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recon: Reducing Conflicting Gradients From the Root For Multi-Task Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Recursive Time Series Data Augmentation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Red PANDA: Disambiguating Image Anomaly Detection by Removing Nuisance Factors |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Regression with Label Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relational Attention: Generalizing Transformers for Graph-Structured Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relative representations enable zero-shot latent space communication |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reliability of CKA as a Similarity Measure in Deep Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reparameterization through Spatial Gradient Scaling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Replicable Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Representation Learning for Low-rank General-sum Markov Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Representational Dissimilarity Metric Spaces for Stochastic Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Restricted Strong Convexity of Deep Learning Models with Smooth Activations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rethinking Graph Lottery Tickets: Graph Sparsity Matters |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Rethinking skip connection model as a learnable Markov chain |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Rethinking the Expressive Power of GNNs via Graph Biconnectivity |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Retrieval-based Controllable Molecule Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reversible Column Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Revisiting Populations in multi-agent Communication |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Robustness in Graph Machine Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting adapters with adversarial training |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Revisiting the Assumption of Latent Separability for Backdoor Defenses |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Revisiting the Entropy Semiring for Neural Speech Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reward Design with Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Riemannian Metric Learning via Optimal Transport |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non-linear Function Approximation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust Active Distillation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust Algorithms on Adaptive Inputs from Bounded Adversaries |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Robust Explanation Constraints for Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Fair Clustering: A Novel Fairness Attack and Defense Framework |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Graph Dictionary Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Scheduling with GFlowNets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust and Controllable Object-Centric Learning through Energy-based Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robustness to corruption in pre-trained Bayesian neural networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| S-NeRF: Neural Radiance Fields for Street Views |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SAM as an Optimal Relaxation of Bayes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SCoMoE: Efficient Mixtures of Experts with Structured Communication |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SGDA with shuffling: faster convergence for nonconvex-PŁ minimax optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SIMPLE: A Gradient Estimator for k-Subset Sampling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SIMPLE: Specialized Model-Sample Matching for Domain Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SLTUNET: A Simple Unified Model for Sign Language Translation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SMART: Self-supervised Multi-task pretrAining with contRol Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SMART: Sentences as Basic Units for Text Evaluation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SP2 : A Second Order Stochastic Polyak Method |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SQA3D: Situated Question Answering in 3D Scenes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| STREET: A MULTI-TASK STRUCTURED REASONING AND EXPLANATION BENCHMARK |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| STaSy: Score-based Tabular data Synthesis |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SYNC: SAFETY-AWARE NEURAL CONTROL FOR STABILIZING STOCHASTIC DELAY-DIFFERENTIAL EQUATIONS |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Safe Reinforcement Learning From Pixels Using a Stochastic Latent Representation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sampling with Mollified Interaction Energy Descent |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sampling-based inference for large linear models, with application to linearised Laplace |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sampling-free Inference for Ab-Initio Potential Energy Surface Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaffolding a Student to Instill Knowledge |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scalable Subset Sampling with Neural Conditional Poisson Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaling Forward Gradient With Local Losses |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Scaling Laws For Deep Learning Based Image Reconstruction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling Laws for a Multi-Agent Reinforcement Learning Model |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scaling Pareto-Efficient Decision Making via Offline Multi-Objective RL |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Scaling Up Probabilistic Circuits by Latent Variable Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Scenario-based Question Answering with Interacting Contextual Properties |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Schema Inference for Interpretable Image Classification |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Score-based Continuous-time Discrete Diffusion Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Selective Annotation Makes Language Models Better Few-Shot Learners |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Selective Frequency Network for Image Restoration |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Self-Consistency Improves Chain of Thought Reasoning in Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Self-Distillation for Further Pre-training of Transformers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Supervised Set Representation Learning for Unsupervised Meta-Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Self-supervised learning with rotation-invariant kernels |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-supervision through Random Segments with Autoregressive Coding (RandSAC) |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SemPPL: Predicting Pseudo-Labels for Better Contrastive Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Semi-Implicit Variational Inference via Score Matching |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semi-Parametric Inducing Point Networks and Neural Processes |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semi-supervised Community Detection via Structural Similarity Metrics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-supervised learning with a principled likelihood from a generative model of data curation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sequential Attention for Feature Selection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sequential Gradient Coding For Straggler Mitigation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sequential Learning of Neural Networks for Prequential MDL |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Serving Graph Compression for Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Short-Term Memory Convolutions |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sign and Basis Invariant Networks for Spectral Graph Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SimPer: Simple Self-Supervised Learning of Periodic Targets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Simple Emergent Action Representations from Multi-Task Policy Training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Simple and Scalable Nearest Neighbor Machine Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Simplicial Embeddings in Self-Supervised Learning and Downstream Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Simplicial Hopfield networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Simplified State Space Layers for Sequence Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Single-shot General Hyper-parameter Optimization for Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SketchKnitter: Vectorized Sketch Generation with Diffusion Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Softened Symbol Grounding for Neuro-symbolic Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Solving Constrained Variational Inequalities via a First-order Interior Point-based Method |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Solving Continuous Control via Q-learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Solving stochastic weak Minty variational inequalities without increasing batch size |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sound Randomized Smoothing in Floating-Point Arithmetic |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Spacetime Representation Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparse Distributed Memory is a Continual Learner |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sparse Mixture-of-Experts are Domain Generalizable Learners |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sparse Random Networks for Communication-Efficient Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse Token Transformer with Attention Back Tracking |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse tree-based Initialization for Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparsity-Constrained Optimal Transport |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Spatial Attention Kinetic Networks with E(n)-Equivariance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Spatio-temporal point processes with deep non-stationary kernels |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Specformer: Spectral Graph Neural Networks Meet Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Spectral Augmentation for Self-Supervised Learning on Graphs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Spectral Decomposition Representation for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SpeedyZero: Mastering Atari with Limited Data and Time |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Spherical Sliced-Wasserstein |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Spikformer: When Spiking Neural Network Meets Transformer |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spiking Convolutional Neural Networks for Text Classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Squeeze Training for Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Stable Target Field for Reduced Variance Score Estimation in Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Statistical Efficiency of Score Matching: The View from Isoperimetry |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Statistical Guarantees for Consensus Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistical Inference for Fisher Market Equilibrium |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stay Moral and Explore: Learn to Behave Morally in Text-based Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Differentially Private and Fair Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Multi-Person 3D Motion Forecasting |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Stochastic No-regret Learning for General Games with Variance Reduction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Strategic Classification with Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Strong inductive biases provably prevent harmless interpolation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Structure by Architecture: Structured Representations without Regularization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| StyleMorph: Disentangled 3D-Aware Image Synthesis with a 3D Morphable StyleGAN |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Subsampling in Large Graphs Using Ricci Curvature |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Supervision Complexity and its Role in Knowledge Distillation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Suppressing the Heterogeneity: A Strong Feature Extractor for Few-shot Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Surgical Fine-Tuning Improves Adaptation to Distribution Shifts |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Switch-NeRF: Learning Scene Decomposition with Mixture of Experts for Large-scale Neural Radiance Fields |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Symmetric Pruning in Quantum Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Synthetic Data Generation of Many-to-Many Datasets via Random Graph Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Systematic Rectification of Language Models via Dead-end Analysis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| TEMPERA: Test-Time Prompt Editing via Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| TabCaps: A Capsule Neural Network for Tabular Data Classification with BoW Routing |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Tailoring Language Generation Models under Total Variation Distance |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Task Ambiguity in Humans and Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Task-Aware Information Routing from Common Representation Space in Lifelong Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene Understanding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Teacher Guided Training: An Efficient Framework for Knowledge Transfer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| TempCLR: Temporal Alignment Representation with Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Temperature Schedules for self-supervised contrastive methods on long-tail data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Temporal Coherent Test Time Optimization for Robust Video Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Temporal Dependencies in Feature Importance for Time Series Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams. |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Test-Time Adaptation via Self-Training with Nearest Neighbor Information |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Test-Time Robust Personalization for Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Text Summarization with Oracle Expectation |
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6 |
| TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization |
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5 |
| TextShield: Beyond Successfully Detecting Adversarial Sentences in text classification |
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4 |
| Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations |
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4 |
| That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation |
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3 |
| The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks |
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3 |
| The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image |
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5 |
| The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation |
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4 |
| The Curious Case of Benign Memorization |
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3 |
| The Dark Side of AutoML: Towards Architectural Backdoor Search |
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4 |
| The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition |
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❌ |
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5 |
| The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks |
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3 |
| The In-Sample Softmax for Offline Reinforcement Learning |
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4 |
| The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks |
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2 |
| The KFIoU Loss for Rotated Object Detection |
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5 |
| The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers |
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3 |
| The Lie Derivative for Measuring Learned Equivariance |
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5 |
| The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation |
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5 |
| The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes |
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3 |
| The Power of Regularization in Solving Extensive-Form Games |
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2 |
| The Provable Benefit of Unsupervised Data Sharing for Offline Reinforcement Learning |
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3 |
| The Role of Coverage in Online Reinforcement Learning |
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❌ |
❌ |
❌ |
1 |
| The Role of ImageNet Classes in Fréchet Inception Distance |
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❌ |
✅ |
6 |
| The Surprising Computational Power of Nondeterministic Stack RNNs |
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4 |
| The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry |
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5 |
| The Symmetric Generalized Eigenvalue Problem as a Nash Equilibrium |
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5 |
| The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection |
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4 |
| The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning |
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4 |
| The hidden uniform cluster prior in self-supervised learning |
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4 |
| Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning |
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✅ |
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❌ |
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3 |
| This Looks Like It Rather Than That: ProtoKNN For Similarity-Based Classifiers |
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❌ |
✅ |
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3 |
| TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization |
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❌ |
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3 |
| Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors |
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✅ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection |
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✅ |
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❌ |
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5 |
| Time to augment self-supervised visual representation learning |
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❌ |
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5 |
| TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis |
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6 |
| Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints |
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❌ |
❌ |
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3 |
| Toeplitz Neural Network for Sequence Modeling |
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❌ |
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5 |
| Token Merging: Your ViT But Faster |
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❌ |
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6 |
| Topology-aware Robust Optimization for Out-of-Distribution Generalization |
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✅ |
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❌ |
❌ |
✅ |
5 |
| Toward Adversarial Training on Contextualized Language Representation |
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❌ |
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6 |
| Towards Addressing Label Skews in One-Shot Federated Learning |
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❌ |
✅ |
6 |
| Towards Better Selective Classification |
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❌ |
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5 |
| Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective |
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❌ |
❌ |
❌ |
✅ |
❌ |
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2 |
| Towards Inferential Reproducibility of Machine Learning Research |
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✅ |
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❌ |
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❌ |
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4 |
| Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes |
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❌ |
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❌ |
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4 |
| Towards Lightweight, Model-Agnostic and Diversity-Aware Active Anomaly Detection |
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4 |
| Towards Minimax Optimal Reward-free Reinforcement Learning in Linear MDPs |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case |
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❌ |
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6 |
| Towards Open Temporal Graph Neural Networks |
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❌ |
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5 |
| Towards Robust Object Detection Invariant to Real-World Domain Shifts |
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❌ |
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4 |
| Towards Robustness Certification Against Universal Perturbations |
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❌ |
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5 |
| Towards Smooth Video Composition |
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4 |
| Towards Stable Test-time Adaptation in Dynamic Wild World |
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5 |
| Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning |
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❌ |
✅ |
❌ |
❌ |
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1 |
| Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation |
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2 |
| Towards Understanding Why Mask Reconstruction Pretraining Helps in Downstream Tasks |
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❌ |
✅ |
❌ |
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1 |
| Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning |
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5 |
| Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism |
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5 |
| Towards convergence to Nash equilibria in two-team zero-sum games |
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2 |
| Towards the Generalization of Contrastive Self-Supervised Learning |
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✅ |
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2 |
| Trading Information between Latents in Hierarchical Variational Autoencoders |
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4 |
| Trainability Preserving Neural Pruning |
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6 |
| Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions |
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5 |
| Training language models to summarize narratives improves brain alignment |
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5 |
| Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis |
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❌ |
❌ |
❌ |
✅ |
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4 |
| TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation |
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❌ |
✅ |
❌ |
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❌ |
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3 |
| Transfer Learning with Deep Tabular Models |
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5 |
| Transfer NAS with Meta-learned Bayesian Surrogates |
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❌ |
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5 |
| Transferable Unlearnable Examples |
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❌ |
❌ |
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4 |
| Transformer Meets Boundary Value Inverse Problems |
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❌ |
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5 |
| Transformer-Patcher: One Mistake Worth One Neuron |
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❌ |
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5 |
| Transformer-based World Models Are Happy With 100k Interactions |
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❌ |
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❌ |
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5 |
| Transformer-based model for symbolic regression via joint supervised learning |
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5 |
| Transformers Learn Shortcuts to Automata |
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3 |
| Transformers are Sample-Efficient World Models |
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5 |
| Treeformer: Dense Gradient Trees for Efficient Attention Computation |
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4 |
| TrojText: Test-time Invisible Textual Trojan Insertion |
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❌ |
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5 |
| Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders |
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6 |
| Truthful Self-Play |
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3 |
| Tuning Frequency Bias in Neural Network Training with Nonuniform Data |
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✅ |
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❌ |
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2 |
| Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection |
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❌ |
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3 |
| TypeT5: Seq2seq Type Inference using Static Analysis |
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5 |
| UL2: Unifying Language Learning Paradigms |
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6 |
| UNICORN: A Unified Backdoor Trigger Inversion Framework |
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5 |
| UNIFIED-IO: A Unified Model for Vision, Language, and Multi-modal Tasks |
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3 |
| Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States |
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5 |
| Unbiased Supervised Contrastive Learning |
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❌ |
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5 |
| Understanding DDPM Latent Codes Through Optimal Transport |
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3 |
| Understanding Edge-of-Stability Training Dynamics with a Minimalist Example |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| Understanding Embodied Reference with Touch-Line Transformer |
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5 |
| Understanding Influence Functions and Datamodels via Harmonic Analysis |
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3 |
| Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles |
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❌ |
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3 |
| Understanding The Robustness of Self-supervised Learning Through Topic Modeling |
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4 |
| Understanding Train-Validation Split in Meta-Learning with Neural Networks |
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✅ |
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3 |
| Understanding Why Generalized Reweighting Does Not Improve Over ERM |
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✅ |
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2 |
| Understanding Zero-shot Adversarial Robustness for Large-Scale Models |
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4 |
| Understanding and Adopting Rational Behavior by Bellman Score Estimation |
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2 |
| Understanding new tasks through the lens of training data via exponential tilting |
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5 |
| Understanding the Covariance Structure of Convolutional Filters |
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3 |
| Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization |
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❌ |
✅ |
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❌ |
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2 |
| Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| Understanding weight-magnitude hyperparameters in training binary networks |
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4 |
| Uni-Mol: A Universal 3D Molecular Representation Learning Framework |
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❌ |
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6 |
| UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph |
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4 |
| UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining |
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6 |
| Unicom: Universal and Compact Representation Learning for Image Retrieval |
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❌ |
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5 |
| Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization |
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4 |
| Unified Discrete Diffusion for Simultaneous Vision-Language Generation |
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4 |
| Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching |
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❌ |
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4 |
| Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval |
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❌ |
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4 |
| Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? |
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❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised 3D Object Learning through Neuron Activity aware Plasticity |
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❌ |
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❌ |
❌ |
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2 |
| Unsupervised Learning for Combinatorial Optimization Needs Meta Learning |
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7 |
| Unsupervised Manifold Alignment with Joint Multidimensional Scaling |
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❌ |
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❌ |
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5 |
| Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning |
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❌ |
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6 |
| Unsupervised Model Selection for Time Series Anomaly Detection |
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6 |
| Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations |
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❌ |
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6 |
| Unsupervised visualization of image datasets using contrastive learning |
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6 |
| Unveiling the sampling density in non-uniform geometric graphs |
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❌ |
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❌ |
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3 |
| User-Interactive Offline Reinforcement Learning |
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❌ |
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6 |
| Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks |
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5 |
| Using Language to Extend to Unseen Domains |
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5 |
| VA-DepthNet: A Variational Approach to Single Image Depth Prediction |
❌ |
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✅ |
✅ |
6 |
| VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training |
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❌ |
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4 |
| VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation |
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5 |
| Valid P-Value for Deep Learning-driven Salient Region |
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4 |
| Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning |
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5 |
| Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top |
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6 |
| Variance-Aware Sparse Linear Bandits |
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1 |
| Variational Information Pursuit for Interpretable Predictions |
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5 |
| Variational Latent Branching Model for Off-Policy Evaluation |
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5 |
| Verifying the Union of Manifolds Hypothesis for Image Data |
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5 |
| Versatile Neural Processes for Learning Implicit Neural Representations |
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2 |
| Video Scene Graph Generation from Single-Frame Weak Supervision |
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3 |
| View Synthesis with Sculpted Neural Points |
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5 |
| ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency |
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4 |
| Vision Transformer Adapter for Dense Predictions |
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4 |
| Visual Classification via Description from Large Language Models |
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3 |
| Visual Imitation Learning with Patch Rewards |
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4 |
| Visual Recognition with Deep Nearest Centroids |
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6 |
| Visually-Augmented Language Modeling |
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4 |
| VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis |
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4 |
| Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding |
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5 |
| Volumetric Optimal Transportation by Fast Fourier Transform |
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3 |
| Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction |
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3 |
| Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning |
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5 |
| Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees |
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6 |
| Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic |
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4 |
| Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection |
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5 |
| Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning |
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5 |
| Weighted Clock Logic Point Process |
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7 |
| Weighted Ensemble Self-Supervised Learning |
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5 |
| What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? |
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5 |
| What Do Self-Supervised Vision Transformers Learn? |
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5 |
| What Is Missing in IRM Training and Evaluation? Challenges and Solutions |
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4 |
| What Makes Convolutional Models Great on Long Sequence Modeling? |
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5 |
| What shapes the loss landscape of self supervised learning? |
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2 |
| When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning |
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5 |
| When Source-Free Domain Adaptation Meets Learning with Noisy Labels |
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3 |
| When and Why Vision-Language Models Behave like Bags-Of-Words, and What to Do About It? |
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5 |
| When to Make and Break Commitments? |
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3 |
| Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning |
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4 |
| Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions |
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3 |
| Which Layer is Learning Faster? A Systematic Exploration of Layer-wise Convergence Rate for Deep Neural Networks |
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2 |
| Why (and When) does Local SGD Generalize Better than SGD? |
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6 |
| Why adversarial training can hurt robust accuracy |
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3 |
| WiNeRT: Towards Neural Ray Tracing for Wireless Channel Modelling and Differentiable Simulations |
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4 |
| WikiWhy: Answering and Explaining Cause-and-Effect Questions |
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4 |
| Win: Weight-Decay-Integrated Nesterov Acceleration for Adaptive Gradient Algorithms |
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4 |
| Winning Both the Accuracy of Floating Point Activation and the Simplicity of Integer Arithmetic |
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3 |
| Words are all you need? Language as an approximation for human similarity judgments |
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4 |
| Write and Paint: Generative Vision-Language Models are Unified Modal Learners |
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5 |
| Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding |
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3 |
| Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model |
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5 |
| Zeroth-Order Optimization with Trajectory-Informed Derivative Estimation |
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4 |
| ZiCo: Zero-shot NAS via inverse Coefficient of Variation on Gradients |
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6 |
| f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation |
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3 |
| gDDIM: Generalized denoising diffusion implicit models |
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5 |
| kNN-Diffusion: Image Generation via Large-Scale Retrieval |
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5 |
| simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing |
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5 |
| wav2tok: Deep Sequence Tokenizer for Audio Retrieval |
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4 |
| What learning algorithm is in-context learning? Investigations with linear models |
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3 |