| A Baseline for Few-Shot Image Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| A Closer Look at Deep Policy Gradients |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Closer Look at the Optimization Landscapes of Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Constructive Prediction of the Generalization Error Across Scales |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Fair Comparison of Graph Neural Networks for Graph Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Generalized Training Approach for Multiagent Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Latent Morphology Model for Open-Vocabulary Neural Machine Translation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Learning-based Iterative Method for Solving Vehicle Routing Problems |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Mutual Information Maximization Perspective of Language Representation Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Probabilistic Formulation of Unsupervised Text Style Transfer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Signal Propagation Perspective for Pruning Neural Networks at Initialization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Stochastic Derivative Free Optimization Method with Momentum |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Theoretical Analysis of the Number of Shots in Few-Shot Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Theory of Usable Information under Computational Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A closer look at the approximation capabilities of neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A critical analysis of self-supervision, or what we can learn from a single image |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| AE-OT: A NEW GENERATIVE MODEL BASED ON EXTENDED SEMI-DISCRETE OPTIMAL TRANSPORT |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| AMRL: Aggregated Memory For Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Abductive Commonsense Reasoning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Abstract Diagrammatic Reasoning with Multiplex Graph Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Accelerating SGD with momentum for over-parameterized learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Action Semantics Network: Considering the Effects of Actions in Multiagent Systems |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Adaptive Structural Fingerprints for Graph Attention Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adjustable Real-time Style Transfer |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adversarial AutoAugment |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adversarial Lipschitz Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Policies: Attacking Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adversarial Training and Provable Defenses: Bridging the Gap |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Adversarially Robust Representations with Smooth Encoders |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarially robust transfer learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Exponential Learning Rate Schedule for Deep Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| And the Bit Goes Down: Revisiting the Quantization of Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Are Transformers universal approximators of sequence-to-sequence functions? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Asymptotics of Wide Networks from Feynman Diagrams |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| AtomNAS: Fine-Grained End-to-End Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Augmenting Non-Collaborative Dialog Systems with Explicit Semantic and Strategic Dialog History |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| AutoQ: Automated Kernel-Wise Neural Network Quantization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Automated Relational Meta-learning |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Automated curriculum generation through setter-solver interactions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Automatically Discovering and Learning New Visual Categories with Ranking Statistics |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| B-Spline CNNs on Lie groups |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| BERTScore: Evaluating Text Generation with BERT |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| BackPACK: Packing more into Backprop |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Batch-shaping for learning conditional channel gated networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| BayesOpt Adversarial Attack |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Meta Sampling for Fast Uncertainty Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Behaviour Suite for Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Black-Box Adversarial Attack with Transferable Model-based Embedding |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bounds on Over-Parameterization for Guaranteed Existence of Descent Paths in Shallow ReLU Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Building Deep Equivariant Capsule Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CAQL: Continuous Action Q-Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CLEVRER: Collision Events for Video Representation and Reasoning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| CLN2INV: Learning Loop Invariants with Continuous Logic Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Can gradient clipping mitigate label noise? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Capsules with Inverted Dot-Product Attention Routing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causal Discovery with Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Certified Defenses for Adversarial Patches |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Classification-Based Anomaly Detection for General Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| CoPhy: Counterfactual Learning of Physical Dynamics |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Combining Q-Learning and Search with Amortized Value Estimates |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Comparing Rewinding and Fine-tuning in Neural Network Pruning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Composing Task-Agnostic Policies with Deep Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Composition-based Multi-Relational Graph Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compositional Language Continual Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compositional languages emerge in a neural iterated learning model |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compressive Transformers for Long-Range Sequence Modelling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Computation Reallocation for Object Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Conditional Learning of Fair Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Conservative Uncertainty Estimation By Fitting Prior Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Consistency Regularization for Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Continual Learning with Adaptive Weights (CLAW) |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continual Learning with Bayesian Neural Networks for Non-Stationary Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continual learning with hypernetworks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Contrastive Learning of Structured World Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Contrastive Representation Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Controlling generative models with continuous factors of variations |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Convergence of Gradient Methods on Bilinear Zero-Sum Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convolutional Conditional Neural Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Counterfactuals uncover the modular structure of deep generative models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Critical initialisation in continuous approximations of binary neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Cross-Lingual Ability of Multilingual BERT: An Empirical Study |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Curriculum Loss: Robust Learning and Generalization against Label Corruption |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Curvature Graph Network |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DBA: Distributed Backdoor Attacks against Federated Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| DDSP: Differentiable Digital Signal Processing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data-Independent Neural Pruning via Coresets |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Data-dependent Gaussian Prior Objective for Language Generation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Decentralized Deep Learning with Arbitrary Communication Compression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Decoding As Dynamic Programming For Recurrent Autoregressive Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decoupling Representation and Classifier for Long-Tailed Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep 3D Pan via local adaptive "t-shaped" convolutions with global and local adaptive dilations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Audio Priors Emerge From Harmonic Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Deep Double Descent: Where Bigger Models and More Data Hurt |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Graph Matching Consensus |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Imitative Models for Flexible Inference, Planning, and Control |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Learning For Symbolic Mathematics |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Network Classification by Scattering and Homotopy Dictionary Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Orientation Uncertainty Learning based on a Bingham Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Semi-Supervised Anomaly Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Symbolic Superoptimization Without Human Knowledge |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep neuroethology of a virtual rodent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep probabilistic subsampling for task-adaptive compressed sensing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DeepHoyer: Learning Sparser Neural Network with Differentiable Scale-Invariant Sparsity Measures |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DeepSphere: a graph-based spherical CNN |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DeepV2D: Video to Depth with Differentiable Structure from Motion |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Defending Against Physically Realizable Attacks on Image Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Demystifying Inter-Class Disentanglement |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Depth-Adaptive Transformer |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Detecting Extrapolation with Local Ensembles |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| DiffTaichi: Differentiable Programming for Physical Simulation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Difference-Seeking Generative Adversarial Network--Unseen Sample Generation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Differentiable Reasoning over a Virtual Knowledge Base |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Differentiable learning of numerical rules in knowledge graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Differentially Private Meta-Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentiation of Blackbox Combinatorial Solvers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Directional Message Passing for Molecular Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Disagreement-Regularized Imitation Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discovering Motor Programs by Recomposing Demonstrations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Discriminative Particle Filter Reinforcement Learning for Complex Partial observations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN) |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Disentangling Factors of Variations Using Few Labels |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Disentangling neural mechanisms for perceptual grouping |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Distance-Based Learning from Errors for Confidence Calibration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distributionally Robust Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Diverse Trajectory Forecasting with Determinantal Point Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DivideMix: Learning with Noisy Labels as Semi-supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Domain Adaptive Multibranch Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Don't Use Large Mini-batches, Use Local SGD |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Double Neural Counterfactual Regret Minimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dream to Control: Learning Behaviors by Latent Imagination |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DropEdge: Towards Deep Graph Convolutional Networks on Node Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Duration-of-Stay Storage Assignment under Uncertainty |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Dynamic Model Pruning with Feedback |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dynamic Time Lag Regression: Predicting What & When |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Dynamics-Aware Embeddings |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dynamics-Aware Unsupervised Discovery of Skills |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ES-MAML: Simple Hessian-Free Meta Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Editable Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Effect of Activation Functions on the Training of Overparametrized Neural Nets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Probabilistic Logic Reasoning with Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient and Information-Preserving Future Frame Prediction and Beyond |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Emergent Tool Use From Multi-Agent Autocurricula |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Empirical Bayes Transductive Meta-Learning with Synthetic Gradients |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Empirical Studies on the Properties of Linear Regions in Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Encoding word order in complex embeddings |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| End to End Trainable Active Contours via Differentiable Rendering |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Energy-based models for atomic-resolution protein conformations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhancing Adversarial Defense by k-Winners-Take-All |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Ensemble Distribution Distillation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Environmental drivers of systematicity and generalization in a situated agent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Episodic Reinforcement Learning with Associative Memory |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Escaping Saddle Points Faster with Stochastic Momentum |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating Gradients for Discrete Random Variables by Sampling without Replacement |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Estimating counterfactual treatment outcomes over time through adversarially balanced representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Evaluating The Search Phase of Neural Architecture Search |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Expected Information Maximization: Using the I-Projection for Mixture Density Estimation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Explanation by Progressive Exaggeration |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploration in Reinforcement Learning with Deep Covering Options |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploring Model-based Planning with Policy Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Extreme Classification via Adversarial Softmax Approximation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Extreme Tensoring for Low-Memory Preconditioning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FEW-SHOT LEARNING ON GRAPHS VIA SUPER-CLASSES BASED ON GRAPH SPECTRAL MEASURES |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FSPool: Learning Set Representations with Featurewise Sort Pooling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fair Resource Allocation in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Fantastic Generalization Measures and Where to Find Them |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Neural Network Adaptation via Parameter Remapping and Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast Task Inference with Variational Intrinsic Successor Features |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast is better than free: Revisiting adversarial training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FasterSeg: Searching for Faster Real-time Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Federated Adversarial Domain Adaptation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Federated Learning with Matched Averaging |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Few-shot Text Classification with Distributional Signatures |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Finite Depth and Width Corrections to the Neural Tangent Kernel |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Four Things Everyone Should Know to Improve Batch Normalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FreeLB: Enhanced Adversarial Training for Natural Language Understanding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Frequency-based Search-control in Dyna |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| From Variational to Deterministic Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Functional Regularisation for Continual Learning with Gaussian Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Functional vs. parametric equivalence of ReLU networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GLAD: Learning Sparse Graph Recovery |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Gap-Aware Mitigation of Gradient Staleness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| GenDICE: Generalized Offline Estimation of Stationary Values |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalization bounds for deep convolutional neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Generalization through Memorization: Nearest Neighbor Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generative Models for Effective ML on Private, Decentralized Datasets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generative Ratio Matching Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Geom-GCN: Geometric Graph Convolutional Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Geometric Insights into the Convergence of Nonlinear TD Learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Global Relational Models of Source Code |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Gradient $\ell_1$ Regularization for Quantization Robustness |
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✅ |
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3 |
| Gradient Descent Maximizes the Margin of Homogeneous Neural Networks |
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✅ |
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3 |
| Gradient-Based Neural DAG Learning |
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✅ |
✅ |
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4 |
| Gradientless Descent: High-Dimensional Zeroth-Order Optimization |
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3 |
| Gradients as Features for Deep Representation Learning |
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✅ |
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3 |
| Graph Constrained Reinforcement Learning for Natural Language Action Spaces |
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3 |
| Graph Convolutional Reinforcement Learning |
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3 |
| Graph Neural Networks Exponentially Lose Expressive Power for Node Classification |
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❌ |
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5 |
| Graph inference learning for semi-supervised classification |
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3 |
| GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation |
✅ |
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❌ |
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❌ |
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5 |
| GraphSAINT: Graph Sampling Based Inductive Learning Method |
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✅ |
7 |
| GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding |
✅ |
✅ |
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❌ |
✅ |
❌ |
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5 |
| Guiding Program Synthesis by Learning to Generate Examples |
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✅ |
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❌ |
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3 |
| HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
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4 |
| Hamiltonian Generative Networks |
❌ |
✅ |
❌ |
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❌ |
❌ |
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2 |
| Harnessing Structures for Value-Based Planning and Reinforcement Learning |
✅ |
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❌ |
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❌ |
❌ |
✅ |
3 |
| Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks |
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❌ |
✅ |
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❌ |
❌ |
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3 |
| HiLLoC: lossless image compression with hierarchical latent variable models |
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✅ |
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❌ |
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5 |
| Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| High Fidelity Speech Synthesis with Adversarial Networks |
✅ |
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✅ |
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4 |
| Higher-Order Function Networks for Learning Composable 3D Object Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| How much Position Information Do Convolutional Neural Networks Encode? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How to 0wn the NAS in Your Spare Time |
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5 |
| Hyper-SAGNN: a self-attention based graph neural network for hypergraphs |
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✅ |
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❌ |
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3 |
| Hypermodels for Exploration |
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❌ |
❌ |
❌ |
✅ |
1 |
| I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks |
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❌ |
✅ |
❌ |
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3 |
| Identifying through Flows for Recovering Latent Representations |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Identity Crisis: Memorization and Generalization Under Extreme Overparameterization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Image-guided Neural Object Rendering |
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✅ |
✅ |
❌ |
✅ |
3 |
| Imitation Learning via Off-Policy Distribution Matching |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| Implementation Matters in Deep RL: A Case Study on PPO and TRPO |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Implementing Inductive bias for different navigation tasks through diverse RNN attrractors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Implicit Bias of Gradient Descent based Adversarial Training on Separable Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved memory in recurrent neural networks with sequential non-normal dynamics |
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✅ |
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4 |
| Improving Adversarial Robustness Requires Revisiting Misclassified Examples |
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3 |
| Improving Generalization in Meta Reinforcement Learning using Learned Objectives |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Neural Language Generation with Spectrum Control |
❌ |
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✅ |
✅ |
✅ |
❌ |
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4 |
| In Search for a SAT-friendly Binarized Neural Network Architecture |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Incorporating BERT into Neural Machine Translation |
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✅ |
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✅ |
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❌ |
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5 |
| Inductive Matrix Completion Based on Graph Neural Networks |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inductive and Unsupervised Representation Learning on Graph Structured Objects |
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✅ |
❌ |
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2 |
| Inductive representation learning on temporal graphs |
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✅ |
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4 |
| Infinite-Horizon Differentiable Model Predictive Control |
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✅ |
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3 |
| Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Influence-Based Multi-Agent Exploration |
❌ |
❌ |
❌ |
❌ |
✅ |
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✅ |
2 |
| InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization |
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❌ |
✅ |
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❌ |
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3 |
| Information Geometry of Orthogonal Initializations and Training |
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✅ |
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❌ |
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❌ |
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3 |
| Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models |
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❌ |
✅ |
✅ |
✅ |
✅ |
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5 |
| Intensity-Free Learning of Temporal Point Processes |
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✅ |
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✅ |
❌ |
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4 |
| Interpretable Complex-Valued Neural Networks for Privacy Protection |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Intriguing Properties of Adversarial Training at Scale |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Intrinsic Motivation for Encouraging Synergistic Behavior |
✅ |
❌ |
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❌ |
❌ |
❌ |
✅ |
2 |
| Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems |
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❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Iterative energy-based projection on a normal data manifold for anomaly localization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Jacobian Adversarially Regularized Networks for Robustness |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| Jelly Bean World: A Testbed for Never-Ending Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning |
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❌ |
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❌ |
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3 |
| Kernel of CycleGAN as a principal homogeneous space |
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✅ |
❌ |
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❌ |
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2 |
| Kernelized Wasserstein Natural Gradient |
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✅ |
✅ |
❌ |
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3 |
| Knowledge Consistency between Neural Networks and Beyond |
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❌ |
✅ |
❌ |
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❌ |
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2 |
| LAMOL: LAnguage MOdeling for Lifelong Language Learning |
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✅ |
✅ |
❌ |
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3 |
| LEARNED STEP SIZE QUANTIZATION |
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✅ |
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4 |
| LEARNING EXECUTION THROUGH NEURAL CODE FUSION |
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✅ |
✅ |
❌ |
❌ |
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3 |
| Lagrangian Fluid Simulation with Continuous Convolutions |
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✅ |
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❌ |
✅ |
✅ |
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5 |
| LambdaNet: Probabilistic Type Inference using Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Language GANs Falling Short |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large Batch Optimization for Deep Learning: Training BERT in 76 minutes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings |
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❌ |
✅ |
✅ |
❌ |
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3 |
| Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information |
✅ |
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❌ |
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3 |
| Learn to Explain Efficiently via Neural Logic Inductive Learning |
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4 |
| Learning Compositional Koopman Operators for Model-Based Control |
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2 |
| Learning Disentangled Representations for CounterFactual Regression |
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✅ |
❌ |
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❌ |
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1 |
| Learning Efficient Parameter Server Synchronization Policies for Distributed SGD |
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3 |
| Learning Expensive Coordination: An Event-Based Deep RL Approach |
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3 |
| Learning Heuristics for Quantified Boolean Formulas through Reinforcement Learning |
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❌ |
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5 |
| Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech |
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3 |
| Learning Nearly Decomposable Value Functions Via Communication Minimization |
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3 |
| Learning Robust Representations via Multi-View Information Bottleneck |
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❌ |
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6 |
| Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling |
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3 |
| Learning Space Partitions for Nearest Neighbor Search |
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3 |
| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data |
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✅ |
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4 |
| Learning To Explore Using Active Neural SLAM |
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✅ |
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4 |
| Learning deep graph matching with channel-independent embedding and Hungarian attention |
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✅ |
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2 |
| Learning from Explanations with Neural Execution Tree |
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4 |
| Learning from Rules Generalizing Labeled Exemplars |
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5 |
| Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping |
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✅ |
✅ |
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❌ |
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5 |
| Learning representations for binary-classification without backpropagation |
❌ |
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✅ |
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❌ |
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4 |
| Learning the Arrow of Time for Problems in Reinforcement Learning |
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4 |
| Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Learning to Control PDEs with Differentiable Physics |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| Learning to Coordinate Manipulation Skills via Skill Behavior Diversification |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Learning to Guide Random Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning to Learn by Zeroth-Order Oracle |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Link |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning to Move with Affordance Maps |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Represent Programs with Property Signatures |
✅ |
✅ |
✅ |
❌ |
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❌ |
❌ |
3 |
| Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering |
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✅ |
✅ |
✅ |
❌ |
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4 |
| Learning to solve the credit assignment problem |
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✅ |
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❌ |
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❌ |
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3 |
| Learning transport cost from subset correspondence |
✅ |
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✅ |
✅ |
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❌ |
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4 |
| Learning-Augmented Data Stream Algorithms |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware |
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❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Lipschitz constant estimation of Neural Networks via sparse polynomial optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Lite Transformer with Long-Short Range Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Locality and Compositionality in Zero-Shot Learning |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Logic and the 2-Simplicial Transformer |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Lookahead: A Far-sighted Alternative of Magnitude-based Pruning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Low-Resource Knowledge-Grounded Dialogue Generation |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Low-dimensional statistical manifold embedding of directed graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MEMO: A Deep Network for Flexible Combination of Episodic Memories |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| MMA Training: Direct Input Space Margin Maximization through Adversarial Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Making Efficient Use of Demonstrations to Solve Hard Exploration Problems |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Making Sense of Reinforcement Learning and Probabilistic Inference |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Masked Based Unsupervised Content Transfer |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Massively Multilingual Sparse Word Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Mathematical Reasoning in Latent Space |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Maxmin Q-learning: Controlling the Estimation Bias of Q-learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Measuring Compositional Generalization: A Comprehensive Method on Realistic Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Measuring and Improving the Use of Graph Information in Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Measuring the Reliability of Reinforcement Learning Algorithms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory-Based Graph Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta Dropout: Learning to Perturb Latent Features for Generalization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Meta-Learning Deep Energy-Based Memory Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-Learning with Warped Gradient Descent |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Learning without Memorization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Q-Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-learning curiosity algorithms |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MetaPix: Few-Shot Video Retargeting |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Minimizing FLOPs to Learn Efficient Sparse Representations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mirror-Generative Neural Machine Translation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mixed Precision DNNs: All you need is a good parametrization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mixed-curvature Variational Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Model Based Reinforcement Learning for Atari |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Model-Augmented Actor-Critic: Backpropagating through Paths |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model-based reinforcement learning for biological sequence design |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mogrifier LSTM |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Monotonic Multihead Attention |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Agent Interactions Modeling with Correlated Policies |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-agent Reinforcement Learning for Networked System Control |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multilingual Alignment of Contextual Word Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multiplicative Interactions and Where to Find Them |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mutual Information Gradient Estimation for Representation Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| N-BEATS: Neural basis expansion analysis for interpretable time series forecasting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| NAS evaluation is frustratingly hard |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Network Deconvolution |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Arithmetic Units |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Epitome Search for Architecture-Agnostic Network Compression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Execution of Graph Algorithms |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Neural Machine Translation with Universal Visual Representation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Neural Module Networks for Reasoning over Text |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Network Branching for Neural Network Verification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Neural Outlier Rejection for Self-Supervised Keypoint Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Policy Gradient Methods: Global Optimality and Rates of Convergence |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neural Stored-program Memory |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Tangents: Fast and Easy Infinite Neural Networks in Python |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Text Generation With Unlikelihood Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural tangent kernels, transportation mappings, and universal approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Never Give Up: Learning Directed Exploration Strategies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-Autoregressive Dialog State Tracking |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Novelty Detection Via Blurring |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Oblique Decision Trees from Derivatives of ReLU Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Observational Overfitting in Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Bonus Based Exploration Methods In The Arcade Learning Environment |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Computation and Generalization of Generative Adversarial Imitation Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Identifiability in Transformers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On Mutual Information Maximization for Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Robustness of Neural Ordinary Differential Equations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Universal Equivariant Set Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| On the "steerability" of generative adversarial networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Convergence of FedAvg on Non-IID Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Equivalence between Positional Node Embeddings and Structural Graph Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| On the Global Convergence of Training Deep Linear ResNets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Need for Topology-Aware Generative Models for Manifold-Based Defenses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Relationship between Self-Attention and Convolutional Layers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Variance of the Adaptive Learning Rate and Beyond |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Weaknesses of Reinforcement Learning for Neural Machine Translation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the interaction between supervision and self-play in emergent communication |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Once-for-All: Train One Network and Specialize it for Efficient Deployment |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal Strategies Against Generative Attacks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimistic Exploration even with a Pessimistic Initialisation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Option Discovery using Deep Skill Chaining |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Order Learning and Its Application to Age Estimation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Overlearning Reveals Sensitive Attributes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PCMC-Net: Feature-based Pairwise Choice Markov Chains |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PROGRESSIVE LEARNING AND DISENTANGLEMENT OF HIERARCHICAL REPRESENTATIONS |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PairNorm: Tackling Oversmoothing in GNNs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pay Attention to Features, Transfer Learn Faster CNNs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Permutation Equivariant Models for Compositional Generalization in Language |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Phase Transitions for the Information Bottleneck in Representation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Picking Winning Tickets Before Training by Preserving Gradient Flow |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Piecewise linear activations substantially shape the loss surfaces of neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Plug and Play Language Models: A Simple Approach to Controlled Text Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Poly-encoders: Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Population-Guided Parallel Policy Search for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pre-training Tasks for Embedding-based Large-scale Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Principled Weight Initialization for Hypernetworks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Probability Calibration for Knowledge Graph Embedding Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Program Guided Agent |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Progressive Memory Banks for Incremental Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Projection-Based Constrained Policy Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Provable Filter Pruning for Efficient Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ProxSGD: Training Structured Neural Networks under Regularization and Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pruned Graph Scattering Transforms |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pure and Spurious Critical Points: a Geometric Study of Linear Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Quantum Algorithms for Deep Convolutional Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Query-efficient Meta Attack to Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| RNA Secondary Structure Prediction By Learning Unrolled Algorithms |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients? |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| RTFM: Generalising to New Environment Dynamics via Reading |
❌ |
❌ |
❌ |
✅ |
❌ |
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2 |
| RaCT: Toward Amortized Ranking-Critical Training For Collaborative Filtering |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| RaPP: Novelty Detection with Reconstruction along Projection Pathway |
✅ |
❌ |
✅ |
✅ |
❌ |
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4 |
| Ranking Policy Gradient |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Real or Not Real, that is the Question |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reanalysis of Variance Reduced Temporal Difference Learning |
✅ |
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❌ |
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❌ |
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3 |
| Reconstructing continuous distributions of 3D protein structure from cryo-EM images |
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4 |
| Recurrent neural circuits for contour detection |
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✅ |
✅ |
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❌ |
✅ |
5 |
| Reducing Transformer Depth on Demand with Structured Dropout |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reformer: The Efficient Transformer |
❌ |
✅ |
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❌ |
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4 |
| Regularizing activations in neural networks via distribution matching with the Wasserstein metric |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Reinforced active learning for image segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Relational State-Space Model for Stochastic Multi-Object Systems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Residual Energy-Based Models for Text Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Restricting the Flow: Information Bottlenecks for Attribution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Rethinking the Hyperparameters for Fine-tuning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Revisiting Self-Training for Neural Sequence Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Ridge Regression: Structure, Cross-Validation, and Sketching |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust And Interpretable Blind Image Denoising Via Bias-Free Convolutional Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Local Features for Improving the Generalization of Adversarial Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Reinforcement Learning for Continuous Control with Model Misspecification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Subspace Recovery Layer for Unsupervised Anomaly Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust anomaly detection and backdoor attack detection via differential privacy |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Robust training with ensemble consensus |
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❌ |
❌ |
❌ |
✅ |
3 |
| Robustness Verification for Transformers |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Rotation-invariant clustering of neuronal responses in primary visual cortex |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Rényi Fair Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SAdam: A Variant of Adam for Strongly Convex Functions |
✅ |
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✅ |
❌ |
❌ |
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3 |
| SCALOR: Generative World Models with Scalable Object Representations |
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✅ |
❌ |
❌ |
❌ |
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3 |
| SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference |
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✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| SELF: Learning to Filter Noisy Labels with Self-Ensembling |
✅ |
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✅ |
✅ |
❌ |
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4 |
| SNODE: Spectral Discretization of Neural ODEs for System Identification |
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❌ |
❌ |
❌ |
✅ |
❌ |
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3 |
| SNOW: Subscribing to Knowledge via Channel Pooling for Transfer & Lifelong Learning of Convolutional Neural Networks |
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❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models |
✅ |
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✅ |
✅ |
❌ |
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4 |
| SVQN: Sequential Variational Soft Q-Learning Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample Efficient Policy Gradient Methods with Recursive Variance Reduction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sampling-Free Learning of Bayesian Quantized Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scalable Model Compression by Entropy Penalized Reparameterization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Neural Methods for Reasoning With a Symbolic Knowledge Base |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Scalable and Order-robust Continual Learning with Additive Parameter Decomposition |
✅ |
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✅ |
✅ |
❌ |
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4 |
| Scale-Equivariant Steerable Networks |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Scaling Autoregressive Video Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Selection via Proxy: Efficient Data Selection for Deep Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Self-Adversarial Learning with Comparative Discrimination for Text Generation |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Self-Supervised Learning of Appliance Usage |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Self-labelling via simultaneous clustering and representation learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Semantically-Guided Representation Learning for Self-Supervised Monocular Depth |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Semi-Supervised Generative Modeling for Controllable Speech Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Sharing Knowledge in Multi-Task Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Short and Sparse Deconvolution --- A Geometric Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sign Bits Are All You Need for Black-Box Attacks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sign-OPT: A Query-Efficient Hard-label Adversarial Attack |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Single Episode Policy Transfer in Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum |
✅ |
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✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Smooth markets: A basic mechanism for organizing gradient-based learners |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Smoothness and Stability in GANs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Span Recovery for Deep Neural Networks with Applications to Input Obfuscation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Coding with Gated Learned ISTA |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spectral Embedding of Regularized Block Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Spike-based causal inference for weight alignment |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stable Rank Normalization for Improved Generalization in Neural Networks and GANs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| State Alignment-based Imitation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| State-only Imitation with Transition Dynamics Mismatch |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic AUC Maximization with Deep Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Conditional Generative Networks with Basis Decomposition |
✅ |
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✅ |
❌ |
✅ |
❌ |
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3 |
| Stochastic Weight Averaging in Parallel: Large-Batch Training That Generalizes Well |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Strategies for Pre-training Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding |
❌ |
❌ |
✅ |
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4 |
| StructPool: Structured Graph Pooling via Conditional Random Fields |
✅ |
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✅ |
✅ |
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❌ |
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5 |
| Structured Object-Aware Physics Prediction for Video Modeling and Planning |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sub-policy Adaptation for Hierarchical Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Symplectic Recurrent Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Synthesizing Programmatic Policies that Inductively Generalize |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| TabFact: A Large-scale Dataset for Table-based Fact Verification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Target-Embedding Autoencoders for Supervised Representation Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tensor Decompositions for Temporal Knowledge Base Completion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Break-Even Point on Optimization Trajectories of Deep Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Curious Case of Neural Text Degeneration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Early Phase of Neural Network Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Gambler's Problem and Beyond |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Implicit Bias of Depth: How Incremental Learning Drives Generalization |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Ingredients of Real World Robotic Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Local Elasticity of Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Logical Expressiveness of Graph Neural Networks |
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✅ |
✅ |
✅ |
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❌ |
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4 |
| The Shape of Data: Intrinsic Distance for Data Distributions |
✅ |
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✅ |
❌ |
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5 |
| The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget |
❌ |
❌ |
✅ |
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❌ |
❌ |
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3 |
| The asymptotic spectrum of the Hessian of DNN throughout training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The intriguing role of module criticality in the generalization of deep networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Theory and Evaluation Metrics for Learning Disentangled Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Thieves on Sesame Street! Model Extraction of BERT-based APIs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Thinking While Moving: Deep Reinforcement Learning with Concurrent Control |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
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4 |
| To Relieve Your Headache of Training an MRF, Take AdVIL |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Fast Adaptation of Neural Architectures with Meta Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Stable and Efficient Training of Verifiably Robust Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Towards Verified Robustness under Text Deletion Interventions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Towards a Deep Network Architecture for Structured Smoothness |
❌ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards neural networks that provably know when they don't know |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Training Recurrent Neural Networks Online by Learning Explicit State Variables |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training binary neural networks with real-to-binary convolutions |
❌ |
✅ |
✅ |
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❌ |
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4 |
| Training individually fair ML models with sensitive subspace robustness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Transferable Perturbations of Deep Feature Distributions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Transferring Optimality Across Data Distributions via Homotopy Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tree-Structured Attention with Hierarchical Accumulation |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Truth or backpropaganda? An empirical investigation of deep learning theory |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Uncertainty-guided Continual Learning with Bayesian Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Understanding Architectures Learnt by Cell-based Neural Architecture Search |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Understanding Generalization in Recurrent Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Understanding Knowledge Distillation in Non-autoregressive Machine Translation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Why Neural Networks Generalize Well Through GSNR of Parameters |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding and Improving Information Transfer in Multi-Task Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Understanding and Robustifying Differentiable Architecture Search |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding the Limitations of Conditional Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding the Limitations of Variational Mutual Information Estimators |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universal Approximation with Certified Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unpaired Point Cloud Completion on Real Scans using Adversarial Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unrestricted Adversarial Examples via Semantic Manipulation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unsupervised Clustering using Pseudo-semi-supervised Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Unsupervised Model Selection for Variational Disentangled Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| V4D: 4D Convolutional Neural Networks for Video-level Representation Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| VL-BERT: Pre-training of Generic Visual-Linguistic Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Variance Reduction With Sparse Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Variational Recurrent Models for Solving Partially Observable Control Tasks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Variational Template Machine for Data-to-Text Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Vid2Game: Controllable Characters Extracted from Real-World Videos |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Weakly Supervised Clustering by Exploiting Unique Class Count |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Weakly Supervised Disentanglement with Guarantees |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| What Can Neural Networks Reason About? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| What graph neural networks cannot learn: depth vs width |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| White Noise Analysis of Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| You Only Train Once: Loss-Conditional Training of Deep Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Your classifier is secretly an energy based model and you should treat it like one |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |