| $\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap |
❌ |
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✅ |
✅ |
❌ |
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4 |
| $\mathrm{SO}(2)$-Equivariant Reinforcement Learning |
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2 |
| $\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization |
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❌ |
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5 |
| 8-bit Optimizers via Block-wise Quantization |
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5 |
| A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease |
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4 |
| A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications |
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❌ |
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5 |
| A Comparison of Hamming Errors of Representative Variable Selection Methods |
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1 |
| A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion |
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4 |
| A Deep Variational Approach to Clustering Survival Data |
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5 |
| A Fine-Grained Analysis on Distribution Shift |
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4 |
| A Fine-Tuning Approach to Belief State Modeling |
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❌ |
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4 |
| A First-Occupancy Representation for Reinforcement Learning |
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❌ |
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5 |
| A General Analysis of Example-Selection for Stochastic Gradient Descent |
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7 |
| A Generalized Weighted Optimization Method for Computational Learning and Inversion |
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0 |
| A Johnson-Lindenstrauss Framework for Randomly Initialized CNNs |
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✅ |
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2 |
| A Loss Curvature Perspective on Training Instabilities of Deep Learning Models |
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4 |
| A NON-PARAMETRIC REGRESSION VIEWPOINT : GENERALIZATION OF OVERPARAMETRIZED DEEP RELU NETWORK UNDER NOISY OBSERVATIONS |
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❌ |
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❌ |
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1 |
| A Neural Tangent Kernel Perspective of Infinite Tree Ensembles |
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✅ |
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❌ |
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5 |
| A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" |
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✅ |
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✅ |
❌ |
❌ |
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4 |
| A Program to Build E(N)-Equivariant Steerable CNNs |
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✅ |
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❌ |
❌ |
✅ |
5 |
| A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning |
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1 |
| A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning |
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4 |
| A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks |
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5 |
| A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model |
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❌ |
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❌ |
✅ |
3 |
| A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features |
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❌ |
✅ |
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5 |
| A Theory of Tournament Representations |
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2 |
| A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| A Unified Wasserstein Distributional Robustness Framework for Adversarial Training |
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✅ |
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❌ |
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❌ |
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4 |
| A Zest of LIME: Towards Architecture-Independent Model Distances |
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❌ |
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4 |
| A fast and accurate splitting method for optimal transport: analysis and implementation |
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5 |
| A generalization of the randomized singular value decomposition |
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4 |
| A global convergence theory for deep ReLU implicit networks via over-parameterization |
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✅ |
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❌ |
❌ |
1 |
| ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models |
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❌ |
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5 |
| AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis |
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✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity |
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✅ |
✅ |
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❌ |
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5 |
| AS-MLP: An Axial Shifted MLP Architecture for Vision |
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❌ |
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6 |
| Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions |
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✅ |
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❌ |
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4 |
| Accelerated Policy Learning with Parallel Differentiable Simulation |
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5 |
| Acceleration of Federated Learning with Alleviated Forgetting in Local Training |
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6 |
| Active Hierarchical Exploration with Stable Subgoal Representation Learning |
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5 |
| Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game |
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5 |
| Actor-critic is implicitly biased towards high entropy optimal policies |
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1 |
| Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space |
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4 |
| AdaAug: Learning Class- and Instance-adaptive Data Augmentation Policies |
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6 |
| AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation |
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4 |
| AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning |
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5 |
| Adaptive Wavelet Transformer Network for 3D Shape Representation Learning |
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4 |
| Adversarial Retriever-Ranker for Dense Text Retrieval |
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5 |
| Adversarial Robustness Through the Lens of Causality |
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3 |
| Adversarial Support Alignment |
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6 |
| Adversarial Unlearning of Backdoors via Implicit Hypergradient |
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6 |
| Adversarially Robust Conformal Prediction |
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6 |
| Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations |
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❌ |
✅ |
❌ |
❌ |
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2 |
| AlphaZero-based Proof Cost Network to Aid Game Solving |
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4 |
| Amortized Implicit Differentiation for Stochastic Bilevel Optimization |
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4 |
| Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design |
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6 |
| An Agnostic Approach to Federated Learning with Class Imbalance |
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3 |
| An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch |
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5 |
| An Experimental Design Perspective on Model-Based Reinforcement Learning |
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3 |
| An Explanation of In-context Learning as Implicit Bayesian Inference |
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5 |
| An Information Fusion Approach to Learning with Instance-Dependent Label Noise |
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5 |
| An Operator Theoretic View On Pruning Deep Neural Networks |
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6 |
| An Unconstrained Layer-Peeled Perspective on Neural Collapse |
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2 |
| Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder |
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❌ |
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5 |
| Anisotropic Random Feature Regression in High Dimensions |
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❌ |
❌ |
❌ |
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1 |
| Anomaly Detection for Tabular Data with Internal Contrastive Learning |
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4 |
| Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy |
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5 |
| Anti-Concentrated Confidence Bonuses For Scalable Exploration |
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5 |
| Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice |
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❌ |
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4 |
| Anytime Dense Prediction with Confidence Adaptivity |
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❌ |
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5 |
| Approximation and Learning with Deep Convolutional Models: a Kernel Perspective |
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4 |
| Assessing Generalization of SGD via Disagreement |
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❌ |
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2 |
| Associated Learning: an Alternative to End-to-End Backpropagation that Works on CNN, RNN, and Transformer |
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6 |
| Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks |
❌ |
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1 |
| Attacking deep networks with surrogate-based adversarial black-box methods is easy |
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❌ |
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5 |
| Attention-based Interpretability with Concept Transformers |
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❌ |
❌ |
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5 |
| Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable |
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❌ |
❌ |
❌ |
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4 |
| Augmented Sliced Wasserstein Distances |
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5 |
| Auto-Transfer: Learning to Route Transferable Representations |
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❌ |
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5 |
| Auto-scaling Vision Transformers without Training |
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❌ |
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6 |
| Automated Self-Supervised Learning for Graphs |
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6 |
| Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property |
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5 |
| Autonomous Learning of Object-Centric Abstractions for High-Level Planning |
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❌ |
✅ |
❌ |
❌ |
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3 |
| Autonomous Reinforcement Learning: Formalism and Benchmarking |
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❌ |
❌ |
❌ |
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3 |
| Autoregressive Diffusion Models |
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6 |
| Autoregressive Quantile Flows for Predictive Uncertainty Estimation |
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❌ |
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3 |
| Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning |
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6 |
| BAM: Bayes with Adaptive Memory |
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❌ |
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3 |
| BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis |
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❌ |
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6 |
| BEiT: BERT Pre-Training of Image Transformers |
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5 |
| Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future |
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5 |
| Backdoor Defense via Decoupling the Training Process |
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❌ |
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4 |
| BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models |
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❌ |
❌ |
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4 |
| Bag of Instances Aggregation Boosts Self-supervised Distillation |
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❌ |
❌ |
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4 |
| Bandit Learning with Joint Effect of Incentivized Sampling, Delayed Sampling Feedback, and Self-Reinforcing User Preferences |
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✅ |
❌ |
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❌ |
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3 |
| Bayesian Framework for Gradient Leakage |
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❌ |
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❌ |
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4 |
| Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How |
✅ |
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❌ |
❌ |
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5 |
| Bayesian Neural Network Priors Revisited |
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4 |
| Benchmarking the Spectrum of Agent Capabilities |
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3 |
| Better Supervisory Signals by Observing Learning Paths |
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❌ |
❌ |
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5 |
| Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Bi-linear Value Networks for Multi-goal Reinforcement Learning |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| BiBERT: Accurate Fully Binarized BERT |
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4 |
| Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions |
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❌ |
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❌ |
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3 |
| Boosted Curriculum Reinforcement Learning |
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❌ |
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4 |
| Boosting Randomized Smoothing with Variance Reduced Classifiers |
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❌ |
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6 |
| Boosting the Certified Robustness of L-infinity Distance Nets |
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5 |
| Bootstrapped Meta-Learning |
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5 |
| Bootstrapping Semantic Segmentation with Regional Contrast |
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4 |
| Bregman Gradient Policy Optimization |
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❌ |
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4 |
| Bridging Recommendation and Marketing via Recurrent Intensity Modeling |
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5 |
| Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations |
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5 |
| Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps |
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4 |
| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing |
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4 |
| C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks |
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4 |
| CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals |
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7 |
| CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation |
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3 |
| CKConv: Continuous Kernel Convolution For Sequential Data |
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5 |
| CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability |
❌ |
✅ |
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❌ |
❌ |
❌ |
❌ |
2 |
| COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks |
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❌ |
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4 |
| COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation |
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❌ |
❌ |
❌ |
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3 |
| CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing |
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5 |
| CURVATURE-GUIDED DYNAMIC SCALE NETWORKS FOR MULTI-VIEW STEREO |
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✅ |
❌ |
✅ |
5 |
| Can an Image Classifier Suffice For Action Recognition? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views? |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Capturing Structural Locality in Non-parametric Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Case-based reasoning for better generalization in textual reinforcement learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Causal Contextual Bandits with Targeted Interventions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Charformer: Fast Character Transformers via Gradient-based Subword Tokenization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Chemical-Reaction-Aware Molecule Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Chunked Autoregressive GAN for Conditional Waveform Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Churn Reduction via Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Closed-form Sample Probing for Learning Generative Models in Zero-shot Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| CoBERL: Contrastive BERT for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CoMPS: Continual Meta Policy Search |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Coherence-based Label Propagation over Time Series for Accelerated Active Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Collapse by Conditioning: Training Class-conditional GANs with Limited Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ComPhy: Compositional Physical Reasoning of Objects and Events from Videos |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Comparing Distributions by Measuring Differences that Affect Decision Making |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Compositional Attention: Disentangling Search and Retrieval |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compositional Training for End-to-End Deep AUC Maximization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Concurrent Adversarial Learning for Large-Batch Training |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Conditional Contrastive Learning with Kernel |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Conditional Image Generation by Conditioning Variational Auto-Encoders |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Conditional Object-Centric Learning from Video |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Conditioning Sequence-to-sequence Networks with Learned Activations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Consistent Counterfactuals for Deep Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Constrained Physical-Statistics Models for Dynamical System Identification and Prediction |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Constrained Policy Optimization via Bayesian World Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Constraining Linear-chain CRFs to Regular Languages |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Constructing Orthogonal Convolutions in an Explicit Manner |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Context-Aware Sparse Deep Coordination Graphs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Contextualized Scene Imagination for Generative Commonsense Reasoning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continual Learning with Filter Atom Swapping |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Continual Learning with Recursive Gradient Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Continual Normalization: Rethinking Batch Normalization for Online Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Continuous-Time Meta-Learning with Forward Mode Differentiation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Contrastive Fine-grained Class Clustering via Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Controlling Directions Orthogonal to a Classifier |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Controlling the Complexity and Lipschitz Constant improves Polynomial Nets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convergent Graph Solvers |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Convergent and Efficient Deep Q Learning Algorithm |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Coordination Among Neural Modules Through a Shared Global Workspace |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Counterfactual Plans under Distributional Ambiguity |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Creating Training Sets via Weak Indirect Supervision |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Critical Points in Quantum Generative Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Cross-Domain Imitation Learning via Optimal Transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| CrossBeam: Learning to Search in Bottom-Up Program Synthesis |
✅ |
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✅ |
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❌ |
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6 |
| CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention |
✅ |
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✅ |
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❌ |
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6 |
| CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization |
✅ |
❌ |
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❌ |
❌ |
❌ |
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3 |
| CrowdPlay: Crowdsourcing Human Demonstrations for Offline Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Crystal Diffusion Variational Autoencoder for Periodic Material Generation |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Curriculum learning as a tool to uncover learning principles in the brain |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| CycleMLP: A MLP-like Architecture for Dense Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| D-CODE: Discovering Closed-form ODEs from Observed Trajectories |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| DEGREE: Decomposition Based Explanation for Graph Neural Networks |
✅ |
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7 |
| DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting |
✅ |
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✅ |
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❌ |
❌ |
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5 |
| DISCOVERING AND EXPLAINING THE REPRESENTATION BOTTLENECK OF DNNS |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| DISSECT: Disentangled Simultaneous Explanations via Concept Traversals |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| DIVA: Dataset Derivative of a Learning Task |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| DKM: Differentiable k-Means Clustering Layer for Neural Network Compression |
❌ |
❌ |
✅ |
❌ |
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3 |
| DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Data Poisoning Won’t Save You From Facial Recognition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Data-Driven Offline Optimization for Architecting Hardware Accelerators |
✅ |
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❌ |
✅ |
❌ |
❌ |
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3 |
| Data-Efficient Graph Grammar Learning for Molecular Generation |
❌ |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Dealing with Non-Stationarity in MARL via Trust-Region Decomposition |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Declarative nets that are equilibrium models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Deconstructing the Inductive Biases of Hamiltonian Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decoupled Adaptation for Cross-Domain Object Detection |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Deep Attentive Variational Inference |
❌ |
✅ |
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❌ |
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4 |
| Deep AutoAugment |
❌ |
✅ |
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❌ |
❌ |
❌ |
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3 |
| Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Deep Point Cloud Reconstruction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep ReLU Networks Preserve Expected Length |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Defending Against Image Corruptions Through Adversarial Augmentations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Delaunay Component Analysis for Evaluation of Data Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Denoising Likelihood Score Matching for Conditional Score-based Data Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DictFormer: Tiny Transformer with Shared Dictionary |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
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3 |
| Differentiable DAG Sampling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Differentiable Expectation-Maximization for Set Representation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Differentiable Gradient Sampling for Learning Implicit 3D Scene Reconstructions from a Single Image |
❌ |
✅ |
✅ |
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❌ |
❌ |
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4 |
| Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners |
❌ |
✅ |
✅ |
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❌ |
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5 |
| Differentiable Scaffolding Tree for Molecule Optimization |
✅ |
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✅ |
❌ |
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6 |
| Differentially Private Fine-tuning of Language Models |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discovering Invariant Rationales for Graph Neural Networks |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Discovering Latent Concepts Learned in BERT |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discrepancy-Based Active Learning for Domain Adaptation |
✅ |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Discrete Representations Strengthen Vision Transformer Robustness |
✅ |
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✅ |
✅ |
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❌ |
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5 |
| Discriminative Similarity for Data Clustering |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Disentanglement Analysis with Partial Information Decomposition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Distribution Compression in Near-Linear Time |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
5 |
| Distributional Reinforcement Learning with Monotonic Splines |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Distributionally Robust Fair Principal Components via Geodesic Descents |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Distributionally Robust Models with Parametric Likelihood Ratios |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dive Deeper Into Integral Pose Regression |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Divergence-aware Federated Self-Supervised Learning |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Diverse Client Selection for Federated Learning via Submodular Maximization |
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4 |
| Divisive Feature Normalization Improves Image Recognition Performance in AlexNet |
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3 |
| Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs |
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5 |
| Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset |
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4 |
| Do We Need Anisotropic Graph Neural Networks? |
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6 |
| Do deep networks transfer invariances across classes? |
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5 |
| Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features |
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4 |
| Domain Adversarial Training: A Game Perspective |
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7 |
| Domino: Discovering Systematic Errors with Cross-Modal Embeddings |
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4 |
| Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information |
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4 |
| DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals |
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5 |
| Dropout Q-Functions for Doubly Efficient Reinforcement Learning |
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5 |
| Dual Lottery Ticket Hypothesis |
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❌ |
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5 |
| Dynamic Token Normalization improves Vision Transformers |
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❌ |
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5 |
| Dynamics-Aware Comparison of Learned Reward Functions |
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4 |
| EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits |
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4 |
| EViT: Expediting Vision Transformers via Token Reorganizations |
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7 |
| EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression |
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7 |
| Effect of scale on catastrophic forgetting in neural networks |
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3 |
| Effective Model Sparsification by Scheduled Grow-and-Prune Methods |
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❌ |
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6 |
| Efficient Active Search for Combinatorial Optimization Problems |
❌ |
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❌ |
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5 |
| Efficient Computation of Deep Nonlinear Infinite-Width Neural Networks that Learn Features |
❌ |
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❌ |
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5 |
| Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization |
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❌ |
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❌ |
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5 |
| Efficient Neural Causal Discovery without Acyclicity Constraints |
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❌ |
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✅ |
5 |
| Efficient Self-supervised Vision Transformers for Representation Learning |
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❌ |
❌ |
✅ |
5 |
| Efficient Sharpness-aware Minimization for Improved Training of Neural Networks |
✅ |
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❌ |
❌ |
✅ |
5 |
| Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization |
✅ |
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✅ |
7 |
| Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators |
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❌ |
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✅ |
5 |
| Efficient and Differentiable Conformal Prediction with General Function Classes |
✅ |
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❌ |
❌ |
✅ |
5 |
| Efficiently Modeling Long Sequences with Structured State Spaces |
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❌ |
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6 |
| EigenGame Unloaded: When playing games is better than optimizing |
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4 |
| Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums |
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✅ |
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6 |
| Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation |
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✅ |
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✅ |
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4 |
| Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise |
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3 |
| Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling |
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6 |
| Emergent Communication at Scale |
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6 |
| Enabling Arbitrary Translation Objectives with Adaptive Tree Search |
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✅ |
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❌ |
❌ |
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3 |
| Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression |
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5 |
| End-to-End Learning of Probabilistic Hierarchies on Graphs |
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✅ |
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❌ |
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4 |
| Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning |
✅ |
✅ |
✅ |
❌ |
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❌ |
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4 |
| Energy-Inspired Molecular Conformation Optimization |
❌ |
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❌ |
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5 |
| Enhancing Cross-lingual Transfer by Manifold Mixup |
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❌ |
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5 |
| EntQA: Entity Linking as Question Answering |
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5 |
| Entroformer: A Transformer-based Entropy Model for Learned Image Compression |
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4 |
| Environment Predictive Coding for Visual Navigation |
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4 |
| Equivariant Graph Mechanics Networks with Constraints |
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5 |
| Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations |
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5 |
| Equivariant Subgraph Aggregation Networks |
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6 |
| Equivariant Transformers for Neural Network based Molecular Potentials |
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5 |
| Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks |
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5 |
| Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems |
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3 |
| Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent |
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2 |
| Evaluating Disentanglement of Structured Representations |
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5 |
| Evaluating Distributional Distortion in Neural Language Modeling |
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4 |
| Evaluating Model-Based Planning and Planner Amortization for Continuous Control |
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3 |
| Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions |
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3 |
| Evidential Turing Processes |
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6 |
| Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning |
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3 |
| ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning |
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4 |
| Explainable GNN-Based Models over Knowledge Graphs |
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7 |
| Explaining Point Processes by Learning Interpretable Temporal Logic Rules |
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5 |
| Explanations of Black-Box Models based on Directional Feature Interactions |
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5 |
| Exploiting Class Activation Value for Partial-Label Learning |
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5 |
| Exploring Memorization in Adversarial Training |
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4 |
| Exploring extreme parameter compression for pre-trained language models |
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5 |
| Exploring the Limits of Large Scale Pre-training |
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3 |
| Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings |
✅ |
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❌ |
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4 |
| Expressiveness and Approximation Properties of Graph Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability |
✅ |
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❌ |
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4 |
| Extending the WILDS Benchmark for Unsupervised Adaptation |
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6 |
| F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization |
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5 |
| FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations |
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4 |
| FILIP: Fine-grained Interactive Language-Image Pre-Training |
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3 |
| FILM: Following Instructions in Language with Modular Methods |
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5 |
| FP-DETR: Detection Transformer Advanced by Fully Pre-training |
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4 |
| Fair Normalizing Flows |
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7 |
| FairCal: Fairness Calibration for Face Verification |
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5 |
| Fairness Guarantees under Demographic Shift |
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5 |
| Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling |
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6 |
| Fast AdvProp |
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5 |
| Fast Differentiable Matrix Square Root |
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6 |
| Fast Generic Interaction Detection for Model Interpretability and Compression |
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4 |
| Fast Model Editing at Scale |
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7 |
| Fast Regression for Structured Inputs |
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4 |
| Fast topological clustering with Wasserstein distance |
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3 |
| FastSHAP: Real-Time Shapley Value Estimation |
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6 |
| Feature Kernel Distillation |
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4 |
| FedBABU: Toward Enhanced Representation for Federated Image Classification |
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6 |
| FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning |
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3 |
| FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning |
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6 |
| Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients |
✅ |
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6 |
| Few-Shot Backdoor Attacks on Visual Object Tracking |
❌ |
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❌ |
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5 |
| Few-shot Learning via Dirichlet Tessellation Ensemble |
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7 |
| Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks |
❌ |
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5 |
| Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space |
❌ |
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3 |
| Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks |
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❌ |
❌ |
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4 |
| Finding an Unsupervised Image Segmenter in each of your Deep Generative Models |
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❌ |
❌ |
❌ |
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3 |
| Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution |
❌ |
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4 |
| Fine-grained Differentiable Physics: A Yarn-level Model for Fabrics |
❌ |
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❌ |
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2 |
| Finetuned Language Models are Zero-Shot Learners |
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5 |
| Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward |
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2 |
| Fixed Neural Network Steganography: Train the images, not the network |
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6 |
| FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes |
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4 |
| Focus on the Common Good: Group Distributional Robustness Follows |
✅ |
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❌ |
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5 |
| Fooling Explanations in Text Classifiers |
✅ |
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❌ |
❌ |
❌ |
❌ |
2 |
| Fortuitous Forgetting in Connectionist Networks |
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4 |
| Frame Averaging for Invariant and Equivariant Network Design |
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5 |
| Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits |
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4 |
| From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation |
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6 |
| From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness |
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5 |
| GATSBI: Generative Adversarial Training for Simulation-Based Inference |
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❌ |
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6 |
| GDA-AM: ON THE EFFECTIVENESS OF SOLVING MIN-IMAX OPTIMIZATION VIA ANDERSON MIXING |
✅ |
✅ |
✅ |
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5 |
| GLASS: GNN with Labeling Tricks for Subgraph Representation Learning |
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5 |
| GNN is a Counter? Revisiting GNN for Question Answering |
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5 |
| GNN-LM: Language Modeling based on Global Contexts via GNN |
❌ |
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3 |
| GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems |
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4 |
| GRAND++: Graph Neural Diffusion with A Source Term |
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4 |
| Gaussian Mixture Convolution Networks |
❌ |
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5 |
| GeneDisco: A Benchmark for Experimental Design in Drug Discovery |
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❌ |
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5 |
| Generalisation in Lifelong Reinforcement Learning through Logical Composition |
✅ |
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❌ |
❌ |
✅ |
2 |
| Generalization Through the Lens of Leave-One-Out Error |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalized Decision Transformer for Offline Hindsight Information Matching |
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❌ |
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4 |
| Generalized Demographic Parity for Group Fairness |
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4 |
| Generalized Kernel Thinning |
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4 |
| Generalized Natural Gradient Flows in Hidden Convex-Concave Games and GANs |
❌ |
✅ |
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❌ |
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3 |
| Generalized rectifier wavelet covariance models for texture synthesis |
❌ |
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❌ |
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3 |
| Generalizing Few-Shot NAS with Gradient Matching |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generative Modeling with Optimal Transport Maps |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Generative Models as a Data Source for Multiview Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generative Principal Component Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Generative Pseudo-Inverse Memory |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Geometric Transformers for Protein Interface Contact Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Geometric and Physical Quantities improve E(3) Equivariant Message Passing |
✅ |
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✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GiraffeDet: A Heavy-Neck Paradigm for Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Goal-Directed Planning via Hindsight Experience Replay |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| GradMax: Growing Neural Networks using Gradient Information |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GradSign: Model Performance Inference with Theoretical Insights |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Gradient Importance Learning for Incomplete Observations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Gradient Information Matters in Policy Optimization by Back-propagating through Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gradient Matching for Domain Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Gradient Step Denoiser for convergent Plug-and-Play |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Granger causal inference on DAGs identifies genomic loci regulating transcription |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Condensation for Graph Neural Networks |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Graph Neural Network Guided Local Search for the Traveling Salesperson Problem |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
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5 |
| Graph Neural Networks with Learnable Structural and Positional Representations |
✅ |
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✅ |
✅ |
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❌ |
✅ |
6 |
| Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph-Guided Network for Irregularly Sampled Multivariate Time Series |
❌ |
✅ |
✅ |
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❌ |
❌ |
✅ |
4 |
| Graph-Relational Domain Adaptation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Graph-based Nearest Neighbor Search in Hyperbolic Spaces |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graph-less Neural Networks: Teaching Old MLPs New Tricks Via Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graphon based Clustering and Testing of Networks: Algorithms and Theory |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| GreaseLM: Graph REASoning Enhanced Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Group equivariant neural posterior estimation |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| HTLM: Hyper-Text Pre-Training and Prompting of Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Half-Inverse Gradients for Physical Deep Learning |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Handling Distribution Shifts on Graphs: An Invariance Perspective |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hierarchical Few-Shot Imitation with Skill Transition Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Variational Memory for Few-shot Learning Across Domains |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| High Probability Generalization Bounds with Fast Rates for Minimax Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Hindsight Foresight Relabeling for Meta-Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Hindsight: Posterior-guided training of retrievers for improved open-ended generation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| How Attentive are Graph Attention Networks? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| How Did the Model Change? Efficiently Assessing Machine Learning API Shifts |
✅ |
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❌ |
✅ |
✅ |
✅ |
6 |
| How Do Vision Transformers Work? |
❌ |
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4 |
| How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning |
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❌ |
❌ |
❌ |
✅ |
3 |
| How Low Can We Go: Trading Memory for Error in Low-Precision Training |
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✅ |
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7 |
| How Much Can CLIP Benefit Vision-and-Language Tasks? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| How Well Does Self-Supervised Pre-Training Perform with Streaming Data? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How many degrees of freedom do we need to train deep networks: a loss landscape perspective |
❌ |
✅ |
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❌ |
✅ |
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5 |
| How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| How to Train Your MAML to Excel in Few-Shot Classification |
✅ |
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✅ |
✅ |
❌ |
❌ |
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5 |
| How to deal with missing data in supervised deep learning? |
❌ |
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❌ |
❌ |
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4 |
| How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Huber Additive Models for Non-stationary Time Series Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation |
✅ |
✅ |
✅ |
❌ |
✅ |
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6 |
| Hybrid Local SGD for Federated Learning with Heterogeneous Communications |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hybrid Random Features |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Hyperparameter Tuning with Renyi Differential Privacy |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| IGLU: Efficient GCN Training via Lazy Updates |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Igeood: An Information Geometry Approach to Out-of-Distribution Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Illiterate DALL-E Learns to Compose |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Image BERT Pre-training with Online Tokenizer |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Imbedding Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Imitation Learning by Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Imitation Learning from Observations under Transition Model Disparity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Implicit Bias of Adversarial Training for Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100 |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Improving Mutual Information Estimation with Annealed and Energy-Based Bounds |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Improving Non-Autoregressive Translation Models Without Distillation |
✅ |
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✅ |
✅ |
❌ |
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6 |
| Improving the Accuracy of Learning Example Weights for Imbalance Classification |
✅ |
❌ |
✅ |
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✅ |
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6 |
| In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Increasing the Cost of Model Extraction with Calibrated Proof of Work |
❌ |
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✅ |
❌ |
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5 |
| Incremental False Negative Detection for Contrastive Learning |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Inductive Relation Prediction Using Analogy Subgraph Embeddings |
✅ |
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✅ |
✅ |
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❌ |
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5 |
| InfinityGAN: Towards Infinite-Pixel Image Synthesis |
✅ |
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✅ |
✅ |
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7 |
| Information Bottleneck: Exact Analysis of (Quantized) Neural Networks |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
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7 |
| Information Prioritization through Empowerment in Visual Model-based RL |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Information-theoretic Online Memory Selection for Continual Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| IntSGD: Adaptive Floatless Compression of Stochastic Gradients |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Interacting Contour Stochastic Gradient Langevin Dynamics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Interpretable Unsupervised Diversity Denoising and Artefact Removal |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Invariant Causal Representation Learning for Out-of-Distribution Generalization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Is High Variance Unavoidable in RL? A Case Study in Continuous Control |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Is Homophily a Necessity for Graph Neural Networks? |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Is Importance Weighting Incompatible with Interpolating Classifiers? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation |
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5 |
| It Takes Two to Tango: Mixup for Deep Metric Learning |
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❌ |
✅ |
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3 |
| Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming |
✅ |
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✅ |
❌ |
✅ |
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5 |
| Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Joint Shapley values: a measure of joint feature importance |
❌ |
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❌ |
✅ |
❌ |
❌ |
3 |
| KL Guided Domain Adaptation |
❌ |
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❌ |
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5 |
| Know Thyself: Transferable Visual Control Policies Through Robot-Awareness |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Know Your Action Set: Learning Action Relations for Reinforcement Learning |
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✅ |
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7 |
| Knowledge Infused Decoding |
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❌ |
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5 |
| Knowledge Removal in Sampling-based Bayesian Inference |
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❌ |
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❌ |
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4 |
| L0-Sparse Canonical Correlation Analysis |
✅ |
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✅ |
✅ |
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❌ |
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5 |
| LEARNING GUARANTEES FOR GRAPH CONVOLUTIONAL NETWORKS ON THE STOCHASTIC BLOCK MODEL |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations |
✅ |
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✅ |
✅ |
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7 |
| LOSSY COMPRESSION WITH DISTRIBUTION SHIFT AS ENTROPY CONSTRAINED OPTIMAL TRANSPORT |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Label Encoding for Regression Networks |
❌ |
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✅ |
✅ |
✅ |
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6 |
| Label Leakage and Protection in Two-party Split Learning |
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❌ |
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5 |
| Label-Efficient Semantic Segmentation with Diffusion Models |
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4 |
| Language model compression with weighted low-rank factorization |
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3 |
| Language modeling via stochastic processes |
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4 |
| Language-biased image classification: evaluation based on semantic representations |
❌ |
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❌ |
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2 |
| Language-driven Semantic Segmentation |
❌ |
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✅ |
✅ |
❌ |
✅ |
5 |
| Large Language Models Can Be Strong Differentially Private Learners |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Large-Scale Representation Learning on Graphs via Bootstrapping |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Latent Image Animator: Learning to Animate Images via Latent Space Navigation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learnability Lock: Authorized Learnability Control Through Adversarial Invertible Transformations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learned Simulators for Turbulence |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Altruistic Behaviours in Reinforcement Learning without External Rewards |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Causal Models from Conditional Moment Restrictions by Importance Weighting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Continuous Environment Fields via Implicit Functions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Curves for SGD on Structured Features |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Distributionally Robust Models at Scale via Composite Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Efficient Online 3D Bin Packing on Packing Configuration Trees |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Features with Parameter-Free Layers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Graphon Mean Field Games and Approximate Nash Equilibria |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Learning Hierarchical Structures with Differentiable Nondeterministic Stacks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Long-Term Reward Redistribution via Randomized Return Decomposition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Multimodal VAEs through Mutual Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Neural Contextual Bandits through Perturbed Rewards |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Object-Oriented Dynamics for Planning from Text |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Optimal Conformal Classifiers |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Learning Prototype-oriented Set Representations for Meta-Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Representation from Neural Fisher Kernel with Low-rank Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning State Representations via Retracing in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Strides in Convolutional Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning Super-Features for Image Retrieval |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Synthetic Environments and Reward Networks for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Temporally Causal Latent Processes from General Temporal Data |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning Towards The Largest Margins |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Transferable Reward for Query Object Localization with Policy Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Value Functions from Undirected State-only Experience |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Versatile Neural Architectures by Propagating Network Codes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Weakly-supervised Contrastive Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning a subspace of policies for online adaptation in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning by Directional Gradient Descent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning meta-features for AutoML |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning more skills through optimistic exploration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Annotate Part Segmentation with Gradient Matching |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Complete Code with Sketches |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Learning to Dequantise with Truncated Flows |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Downsample for Segmentation of Ultra-High Resolution Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Extend Molecular Scaffolds with Structural Motifs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning to Generalize across Domains on Single Test Samples |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Guide and to be Guided in the Architect-Builder Problem |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Map for Active Semantic Goal Navigation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Schedule Learning rate with Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning transferable motor skills with hierarchical latent mixture policies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning-Augmented $k$-means Clustering |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Leveraging Automated Unit Tests for Unsupervised Code Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Leveraging unlabeled data to predict out-of-distribution performance |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Linking Emergent and Natural Languages via Corpus Transfer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Lipschitz-constrained Unsupervised Skill Discovery |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LoRA: Low-Rank Adaptation of Large Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Local Feature Swapping for Generalization in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Long Expressive Memory for Sequence Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Looking Back on Learned Experiences For Class/task Incremental Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Lossless Compression with Probabilistic Circuits |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MAML is a Noisy Contrastive Learner in Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MT3: Multi-Task Multitrack Music Transcription |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Map Induction: Compositional spatial submap learning for efficient exploration in novel environments |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mapping Language Models to Grounded Conceptual Spaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mapping conditional distributions for domain adaptation under generalized target shift |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Maximizing Ensemble Diversity in Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Maximum Entropy RL (Provably) Solves Some Robust RL Problems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Maximum n-times Coverage for Vaccine Design |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Measuring CLEVRness: Black-box Testing of Visual Reasoning Models |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Memorizing Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Memory Augmented Optimizers for Deep Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Memory Replay with Data Compression for Continual Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mention Memory: incorporating textual knowledge into Transformers through entity mention attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Message Passing Neural PDE Solvers |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta Discovery: Learning to Discover Novel Classes given Very Limited Data |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Meta-Imitation Learning by Watching Video Demonstrations |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Meta-Learning with Fewer Tasks through Task Interpolation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MetaMorph: Learning Universal Controllers with Transformers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimax Optimization with Smooth Algorithmic Adversaries |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mirror Descent Policy Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Missingness Bias in Model Debugging |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MoReL: Multi-omics Relational Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Model Agnostic Interpretability for Multiple Instance Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Model Zoo: A Growing Brain That Learns Continually |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Model-Based Offline Meta-Reinforcement Learning with Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-augmented Prioritized Experience Replay |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Modeling Label Space Interactions in Multi-label Classification using Box Embeddings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Modular Lifelong Reinforcement Learning via Neural Composition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MonoDistill: Learning Spatial Features for Monocular 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Monotonic Differentiable Sorting Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Agent MDP Homomorphic Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Multi-Critic Actor Learning: Teaching RL Policies to Act with Style |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Mode Deep Matrix and Tensor Factorization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Stage Episodic Control for Strategic Exploration in Text Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Task Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-objective Optimization by Learning Space Partition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multimeasurement Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multitask Prompted Training Enables Zero-Shot Task Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| NASI: Label- and Data-agnostic Neural Architecture Search at Initialization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| NASPY: Automated Extraction of Automated Machine Learning Models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| NETWORK INSENSITIVITY TO PARAMETER NOISE VIA PARAMETER ATTACK DURING TRAINING |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Natural Language Descriptions of Deep Visual Features |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Network Augmentation for Tiny Deep Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NeuPL: Neural Population Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Contextual Bandits with Deep Representation and Shallow Exploration |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Deep Equilibrium Solvers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Link Prediction with Walk Pooling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Methods for Logical Reasoning over Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Models for Output-Space Invariance in Combinatorial Problems |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Networks as Kernel Learners: The Silent Alignment Effect |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Parameter Allocation Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Processes with Stochastic Attention: Paying more attention to the context dataset |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Program Synthesis with Query |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Relational Inference with Node-Specific Information |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Solvers for Fast and Accurate Numerical Optimal Control |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Spectral Marked Point Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Stochastic Dual Dynamic Programming |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neural Structured Prediction for Inductive Node Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Neural Variational Dropout Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural graphical modelling in continuous-time: consistency guarantees and algorithms |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| New Insights on Reducing Abrupt Representation Change in Online Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| No One Representation to Rule Them All: Overlapping Features of Training Methods |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Noisy Feature Mixup |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-Linear Operator Approximations for Initial Value Problems |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Non-Parallel Text Style Transfer with Self-Parallel Supervision |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Nonlinear ICA Using Volume-Preserving Transformations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Normalization of Language Embeddings for Cross-Lingual Alignment |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| OBJECT DYNAMICS DISTILLATION FOR SCENE DECOMPOSITION AND REPRESENTATION |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Object Pursuit: Building a Space of Objects via Discriminative Weight Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Objects in Semantic Topology |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Offline Reinforcement Learning with Implicit Q-Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Offline Reinforcement Learning with Value-based Episodic Memory |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Omni-Dimensional Dynamic Convolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Bridging Generic and Personalized Federated Learning for Image Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Distributed Adaptive Optimization with Gradient Compression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Evaluation Metrics for Graph Generative Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Improving Adversarial Transferability of Vision Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On Incorporating Inductive Biases into VAEs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Non-Random Missing Labels in Semi-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Predicting Generalization using GANs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Redundancy and Diversity in Cell-based Neural Architecture Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Robust Prefix-Tuning for Text Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On feature learning in neural networks with global convergence guarantees |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| On the Certified Robustness for Ensemble Models and Beyond |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Connection between Local Attention and Dynamic Depth-wise Convolution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Convergence of Certified Robust Training with Interval Bound Propagation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On the Convergence of mSGD and AdaGrad for Stochastic Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the Existence of Universal Lottery Tickets |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Importance of Difficulty Calibration in Membership Inference Attacks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Importance of Firth Bias Reduction in Few-Shot Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Learning and Learnability of Quasimetrics |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Limitations of Multimodal VAEs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Optimal Memorization Power of ReLU Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Pitfalls of Analyzing Individual Neurons in Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On the Role of Neural Collapse in Transfer Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Uncomputability of Partition Functions in Energy-Based Sequence Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the approximation properties of recurrent encoder-decoder architectures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the benefits of maximum likelihood estimation for Regression and Forecasting |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the relation between statistical learning and perceptual distances |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the role of population heterogeneity in emergent communication |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| On-Policy Model Errors in Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| One After Another: Learning Incremental Skills for a Changing World |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Online Ad Hoc Teamwork under Partial Observability |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Adversarial Attacks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Coreset Selection for Rehearsal-based Continual Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Facility Location with Predictions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Online Hyperparameter Meta-Learning with Hypergradient Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| OntoProtein: Protein Pretraining With Gene Ontology Embedding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Open-Set Recognition: A Good Closed-Set Classifier is All You Need |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Open-World Semi-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Open-vocabulary Object Detection via Vision and Language Knowledge Distillation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Representations for Covariate Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimal Transport for Causal Discovery |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimal Transport for Long-Tailed Recognition with Learnable Cost Matrix |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Optimization and Adaptive Generalization of Three layer Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimization inspired Multi-Branch Equilibrium Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimizer Amalgamation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimizing Neural Networks with Gradient Lexicase Selection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Orchestrated Value Mapping for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Overcoming The Spectral Bias of Neural Value Approximation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PAC Prediction Sets Under Covariate Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PAC-Bayes Information Bottleneck |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PF-GNN: Differentiable particle filtering based approximation of universal graph representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| POETREE: Interpretable Policy Learning with Adaptive Decision Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parallel Training of GRU Networks with a Multi-Grid Solver for Long Sequences |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pareto Policy Adaptation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Pareto Policy Pool for Model-based Offline Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Particle Stochastic Dual Coordinate Ascent: Exponential convergent algorithm for mean field neural network optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Path Auxiliary Proposal for MCMC in Discrete Space |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Path Integral Sampler: A Stochastic Control Approach For Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Perceiver IO: A General Architecture for Structured Inputs & Outputs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Permutation Compressors for Provably Faster Distributed Nonconvex Optimization |
✅ |
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✅ |
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✅ |
❌ |
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5 |
| Permutation-Based SGD: Is Random Optimal? |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage |
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1 |
| Phase Collapse in Neural Networks |
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5 |
| Phenomenology of Double Descent in Finite-Width Neural Networks |
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3 |
| PiCO: Contrastive Label Disambiguation for Partial Label Learning |
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6 |
| PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication |
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5 |
| Pix2seq: A Language Modeling Framework for Object Detection |
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5 |
| Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models |
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6 |
| Planning in Stochastic Environments with a Learned Model |
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4 |
| Plant 'n' Seek: Can You Find the Winning Ticket? |
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4 |
| PoNet: Pooling Network for Efficient Token Mixing in Long Sequences |
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5 |
| Poisoning and Backdooring Contrastive Learning |
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4 |
| Policy Gradients Incorporating the Future |
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3 |
| Policy Smoothing for Provably Robust Reinforcement Learning |
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6 |
| Policy improvement by planning with Gumbel |
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5 |
| PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions |
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5 |
| Possibility Before Utility: Learning And Using Hierarchical Affordances |
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3 |
| Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation |
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❌ |
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2 |
| Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios |
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❌ |
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5 |
| Practical Conditional Neural Process Via Tractable Dependent Predictions |
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5 |
| Practical Integration via Separable Bijective Networks |
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3 |
| Pre-training Molecular Graph Representation with 3D Geometry |
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4 |
| Predicting Physics in Mesh-reduced Space with Temporal Attention |
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2 |
| Pretrained Language Model in Continual Learning: A Comparative Study |
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5 |
| Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators |
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5 |
| PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior |
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5 |
| Privacy Implications of Shuffling |
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3 |
| Probabilistic Implicit Scene Completion |
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4 |
| Procedural generalization by planning with self-supervised world models |
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3 |
| Programmatic Reinforcement Learning without Oracles |
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4 |
| Progressive Distillation for Fast Sampling of Diffusion Models |
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5 |
| Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection |
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4 |
| Proof Artifact Co-Training for Theorem Proving with Language Models |
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4 |
| Properties from mechanisms: an equivariance perspective on identifiable representation learning |
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❌ |
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1 |
| Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients |
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❌ |
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6 |
| ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics |
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6 |
| Prototype memory and attention mechanisms for few shot image generation |
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5 |
| Prototypical Contrastive Predictive Coding |
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4 |
| Provable Adaptation across Multiway Domains via Representation Learning |
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2 |
| Provable Learning-based Algorithm For Sparse Recovery |
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5 |
| Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics |
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2 |
| Provably Robust Adversarial Examples |
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6 |
| Provably convergent quasistatic dynamics for mean-field two-player zero-sum games |
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2 |
| Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks |
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4 |
| Pseudo Numerical Methods for Diffusion Models on Manifolds |
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5 |
| Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization |
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5 |
| Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting |
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6 |
| QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization |
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5 |
| QUERY EFFICIENT DECISION BASED SPARSE ATTACKS AGAINST BLACK-BOX DEEP LEARNING MODELS |
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4 |
| Quadtree Attention for Vision Transformers |
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4 |
| Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation |
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3 |
| Query Embedding on Hyper-Relational Knowledge Graphs |
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5 |
| R4D: Utilizing Reference Objects for Long-Range Distance Estimation |
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4 |
| R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning |
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4 |
| RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation |
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2 |
| Random matrices in service of ML footprint: ternary random features with no performance loss |
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4 |
| Real-Time Neural Voice Camouflage |
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5 |
| Recursive Disentanglement Network |
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2 |
| Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? |
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5 |
| Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off |
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5 |
| RegionViT: Regional-to-Local Attention for Vision Transformers |
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4 |
| Regularized Autoencoders for Isometric Representation Learning |
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6 |
| Reinforcement Learning in Presence of Discrete Markovian Context Evolution |
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3 |
| Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory |
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4 |
| Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration |
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4 |
| RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning |
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4 |
| Relating transformers to models and neural representations of the hippocampal formation |
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1 |
| Relational Learning with Variational Bayes |
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5 |
| Relational Multi-Task Learning: Modeling Relations between Data and Tasks |
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6 |
| Relational Surrogate Loss Learning |
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6 |
| RelaxLoss: Defending Membership Inference Attacks without Losing Utility |
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6 |
| Reliable Adversarial Distillation with Unreliable Teachers |
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4 |
| Representation Learning for Online and Offline RL in Low-rank MDPs |
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1 |
| Representation-Agnostic Shape Fields |
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5 |
| Representational Continuity for Unsupervised Continual Learning |
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3 |
| Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings |
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5 |
| Resolving Training Biases via Influence-based Data Relabeling |
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6 |
| Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum |
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2 |
| Responsible Disclosure of Generative Models Using Scalable Fingerprinting |
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4 |
| Rethinking Adversarial Transferability from a Data Distribution Perspective |
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5 |
| Rethinking Class-Prior Estimation for Positive-Unlabeled Learning |
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5 |
| Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL |
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5 |
| Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework |
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4 |
| Rethinking Supervised Pre-Training for Better Downstream Transferring |
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3 |
| Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph |
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3 |
| Reverse Engineering of Imperceptible Adversarial Image Perturbations |
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5 |
| Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift |
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7 |
| Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions |
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5 |
| Revisiting Design Choices in Offline Model Based Reinforcement Learning |
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3 |
| Revisiting Over-smoothing in BERT from the Perspective of Graph |
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4 |
| Revisiting flow generative models for Out-of-distribution detection |
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4 |
| Reward Uncertainty for Exploration in Preference-based Reinforcement Learning |
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3 |
| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models |
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3 |
| Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? |
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5 |
| Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning |
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5 |
| Robust and Scalable SDE Learning: A Functional Perspective |
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5 |
| RotoGrad: Gradient Homogenization in Multitask Learning |
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6 |
| RvS: What is Essential for Offline RL via Supervised Learning? |
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5 |
| SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations |
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6 |
| SGD Can Converge to Local Maxima |
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1 |
| SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models |
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6 |
| SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning |
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3 |
| SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training |
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5 |
| SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation |
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5 |
| SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search |
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4 |
| SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning |
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5 |
| Safe Neurosymbolic Learning with Differentiable Symbolic Execution |
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4 |
| Salient ImageNet: How to discover spurious features in Deep Learning? |
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3 |
| Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation |
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5 |
| Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game |
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1 |
| Sample Selection with Uncertainty of Losses for Learning with Noisy Labels |
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5 |
| Sample and Computation Redistribution for Efficient Face Detection |
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6 |
| Sampling with Mirrored Stein Operators |
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6 |
| Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation |
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7 |
| Scalable Sampling for Nonsymmetric Determinantal Point Processes |
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5 |
| Scale Efficiently: Insights from Pretraining and Finetuning Transformers |
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5 |
| Scale Mixtures of Neural Network Gaussian Processes |
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5 |
| Scaling Laws for Neural Machine Translation |
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2 |
| Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption |
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5 |
| Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs |
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2 |
| Scene Transformer: A unified architecture for predicting future trajectories of multiple agents |
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5 |
| Score-Based Generative Modeling with Critically-Damped Langevin Diffusion |
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4 |
| Selective Ensembles for Consistent Predictions |
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6 |
| Self-Joint Supervised Learning |
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6 |
| Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis |
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5 |
| Self-Supervised Inference in State-Space Models |
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4 |
| Self-Supervision Enhanced Feature Selection with Correlated Gates |
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6 |
| Self-ensemble Adversarial Training for Improved Robustness |
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✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Self-supervised Learning is More Robust to Dataset Imbalance |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-relaxed Gromov-Wasserstein divergence and applications on graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Should I Run Offline Reinforcement Learning or Behavioral Cloning? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Shuffle Private Stochastic Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Signing the Supermask: Keep, Hide, Invert |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SimVLM: Simple Visual Language Model Pretraining with Weak Supervision |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SketchODE: Learning neural sketch representation in continuous time |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Skill-based Meta-Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Solving Inverse Problems in Medical Imaging with Score-Based Generative Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sound Adversarial Audio-Visual Navigation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sound and Complete Neural Network Repair with Minimality and Locality Guarantees |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Space-Time Graph Neural Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Spanning Tree-based Graph Generation for Molecules |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sparse Attention with Learning to Hash |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse Communication via Mixed Distributions |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparsity Winning Twice: Better Robust Generalization from More Efficient Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SphereFace2: Binary Classification is All You Need for Deep Face Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spherical Message Passing for 3D Molecular Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Spike-inspired rank coding for fast and accurate recurrent neural networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sqrt(d) Dimension Dependence of Langevin Monte Carlo |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stability Regularization for Discrete Representation Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Steerable Partial Differential Operators for Equivariant Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stein Latent Optimization for Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Step-unrolled Denoising Autoencoders for Text Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stiffness-aware neural network for learning Hamiltonian systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stochastic Training is Not Necessary for Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Strength of Minibatch Noise in SGD |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| StyleAlign: Analysis and Applications of Aligned StyleGAN Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Subspace Regularizers for Few-Shot Class Incremental Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Surrogate Gap Minimization Improves Sharpness-Aware Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Symbolic Learning to Optimize: Towards Interpretability and Scalability |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Synchromesh: Reliable Code Generation from Pre-trained Language Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| TAPEX: Table Pre-training via Learning a Neural SQL Executor |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| TAda! Temporally-Adaptive Convolutions for Video Understanding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TPU-GAN: Learning temporal coherence from dynamic point cloud sequences |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| TRAIL: Near-Optimal Imitation Learning with Suboptimal Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| TRGP: Trust Region Gradient Projection for Continual Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tackling the Generative Learning Trilemma with Denoising Diffusion GANs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Taming Sparsely Activated Transformer with Stochastic Experts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Target-Side Input Augmentation for Sequence to Sequence Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Task Affinity with Maximum Bipartite Matching in Few-Shot Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Task Relatedness-Based Generalization Bounds for Meta Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Task-Induced Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Close Relationship Between Contrastive Learning and Meta-Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Efficiency Misnomer |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| The Evolution of Uncertainty of Learning in Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Information Geometry of Unsupervised Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The MultiBERTs: BERT Reproductions for Robustness Analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Rich Get Richer: Disparate Impact of Semi-Supervised Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| The Role of Pretrained Representations for the OOD Generalization of RL Agents |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| The Spectral Bias of Polynomial Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Three Stages of Learning Dynamics in High-dimensional Kernel Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Uncanny Similarity of Recurrence and Depth |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tighter Sparse Approximation Bounds for ReLU Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Top-N: Equivariant Set and Graph Generation without Exchangeability |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Top-label calibration and multiclass-to-binary reductions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Topological Experience Replay |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Topological Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Topologically Regularized Data Embeddings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Toward Faithful Case-based Reasoning through Learning Prototypes in a Nearest Neighbor-friendly Space. |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Building A Group-based Unsupervised Representation Disentanglement Framework |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Continual Knowledge Learning of Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Empirical Sandwich Bounds on the Rate-Distortion Function |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Evaluating the Robustness of Neural Networks Learned by Transduction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards General Function Approximation in Zero-Sum Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Model Agnostic Federated Learning Using Knowledge Distillation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Understanding Generalization via Decomposing Excess Risk Dynamics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Understanding the Data Dependency of Mixup-style Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Understanding the Robustness Against Evasion Attack on Categorical Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Towards a Unified View of Parameter-Efficient Transfer Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tracking the risk of a deployed model and detecting harmful distribution shifts |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Training Structured Neural Networks Through Manifold Identification and Variance Reduction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Training Transition Policies via Distribution Matching for Complex Tasks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Training invariances and the low-rank phenomenon: beyond linear networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Transfer RL across Observation Feature Spaces via Model-Based Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Transferable Adversarial Attack based on Integrated Gradients |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Transformer Embeddings of Irregularly Spaced Events and Their Participants |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Transformer-based Transform Coding |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Transformers Can Do Bayesian Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Triangle and Four Cycle Counting with Predictions in Graph Streams |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Trigger Hunting with a Topological Prior for Trojan Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond) |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Uncertainty Modeling for Out-of-Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Understanding Dimensional Collapse in Contrastive Self-supervised Learning |
❌ |
✅ |
✅ |
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❌ |
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4 |
| Understanding Domain Randomization for Sim-to-real Transfer |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Understanding Intrinsic Robustness Using Label Uncertainty |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective |
✅ |
✅ |
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❌ |
❌ |
✅ |
5 |
| Understanding and Improving Graph Injection Attack by Promoting Unnoticeability |
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7 |
| Understanding and Leveraging Overparameterization in Recursive Value Estimation |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Understanding and Preventing Capacity Loss in Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Understanding approximate and unrolled dictionary learning for pattern recovery |
✅ |
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✅ |
❌ |
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❌ |
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5 |
| Understanding over-squashing and bottlenecks on graphs via curvature |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Understanding the Role of Self Attention for Efficient Speech Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Understanding the Variance Collapse of SVGD in High Dimensions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unified Visual Transformer Compression |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Unifying Likelihood-free Inference with Black-box Optimization and Beyond |
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❌ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Universal Approximation Under Constraints is Possible with Transformers |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Universalizing Weak Supervision |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unrolling PALM for Sparse Semi-Blind Source Separation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Discovery of Object Radiance Fields |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Unsupervised Disentanglement with Tensor Product Representations on the Torus |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Unsupervised Semantic Segmentation by Distilling Feature Correspondences |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| VAE Approximation Error: ELBO and Exponential Families |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| VC dimension of partially quantized neural networks in the overparametrized regime |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| VOS: Learning What You Don't Know by Virtual Outlier Synthesis |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Value Gradient weighted Model-Based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Variational Neural Cellular Automata |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Variational Predictive Routing with Nested Subjective Timescales |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Variational methods for simulation-based inference |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Variational oracle guiding for reinforcement learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Vector-quantized Image Modeling with Improved VQGAN |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| ViDT: An Efficient and Effective Fully Transformer-based Object Detector |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ViTGAN: Training GANs with Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Vision-Based Manipulators Need to Also See from Their Hands |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Visual Correspondence Hallucination |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Visual Representation Learning Does Not Generalize Strongly Within the Same Domain |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Visual Representation Learning over Latent Domains |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Visual hyperacuity with moving sensor and recurrent neural computations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Vitruvion: A Generative Model of Parametric CAD Sketches |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| W-CTC: a Connectionist Temporal Classification Loss with Wild Cards |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection |
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✅ |
✅ |
✅ |
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❌ |
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5 |
| Weighted Training for Cross-Task Learning |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| What Do We Mean by Generalization in Federated Learning? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| What Happens after SGD Reaches Zero Loss? --A Mathematical Framework |
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❌ |
❌ |
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❌ |
❌ |
0 |
| What Makes Better Augmentation Strategies? Augment Difficult but Not too Different |
✅ |
✅ |
✅ |
✅ |
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❌ |
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6 |
| What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations |
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✅ |
✅ |
✅ |
✅ |
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5 |
| When should agents explore? |
✅ |
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✅ |
✅ |
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5 |
| When, Why, and Which Pretrained GANs Are Useful? |
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5 |
| Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective |
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❌ |
✅ |
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❌ |
✅ |
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3 |
| Who Is Your Right Mixup Partner in Positive and Unlabeled Learning |
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✅ |
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❌ |
❌ |
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4 |
| Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Why Propagate Alone? Parallel Use of Labels and Features on Graphs |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream |
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✅ |
✅ |
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6 |
| Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models |
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✅ |
✅ |
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5 |
| Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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2 |
| X-model: Improving Data Efficiency in Deep Learning with A Minimax Model |
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❌ |
✅ |
✅ |
✅ |
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4 |
| You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction |
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❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Zero Pixel Directional Boundary by Vector Transform |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Zero-Shot Self-Supervised Learning for MRI Reconstruction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| cosFormer: Rethinking Softmax In Attention |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| iFlood: A Stable and Effective Regularizer |
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❌ |
✅ |
✅ |
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❌ |
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4 |
| iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| miniF2F: a cross-system benchmark for formal Olympiad-level mathematics |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| switch-GLAT: Multilingual Parallel Machine Translation Via Code-Switch Decoder |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |