| A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation |
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2 |
| A Closer Look at Few-shot Classification |
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4 |
| A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks |
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2 |
| A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery |
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4 |
| A Direct Approach to Robust Deep Learning Using Adversarial Networks |
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4 |
| A Generative Model For Electron Paths |
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5 |
| A Kernel Random Matrix-Based Approach for Sparse PCA |
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3 |
| A Max-Affine Spline Perspective of Recurrent Neural Networks |
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3 |
| A Mean Field Theory of Batch Normalization |
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2 |
| A Statistical Approach to Assessing Neural Network Robustness |
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4 |
| A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs |
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3 |
| A Universal Music Translation Network |
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3 |
| A Variational Inequality Perspective on Generative Adversarial Networks |
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❌ |
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6 |
| A comprehensive, application-oriented study of catastrophic forgetting in DNNs |
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6 |
| A new dog learns old tricks: RL finds classic optimization algorithms |
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1 |
| A rotation-equivariant convolutional neural network model of primary visual cortex |
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4 |
| A2BCD: Asynchronous Acceleration with Optimal Complexity |
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✅ |
✅ |
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5 |
| ACCELERATING NONCONVEX LEARNING VIA REPLICA EXCHANGE LANGEVIN DIFFUSION |
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❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking |
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✅ |
✅ |
❌ |
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3 |
| ADef: an Iterative Algorithm to Construct Adversarial Deformations |
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❌ |
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5 |
| ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA |
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❌ |
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6 |
| ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION |
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4 |
| ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks |
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❌ |
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6 |
| Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks |
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✅ |
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3 |
| Active Learning with Partial Feedback |
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4 |
| AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods |
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4 |
| Adaptive Estimators Show Information Compression in Deep Neural Networks |
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✅ |
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2 |
| Adaptive Gradient Methods with Dynamic Bound of Learning Rate |
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4 |
| Adaptive Input Representations for Neural Language Modeling |
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5 |
| Adaptive Posterior Learning: few-shot learning with a surprise-based memory module |
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3 |
| Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality |
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0 |
| Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network |
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4 |
| Adversarial Attacks on Graph Neural Networks via Meta Learning |
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3 |
| Adversarial Audio Synthesis |
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6 |
| Adversarial Domain Adaptation for Stable Brain-Machine Interfaces |
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2 |
| Adversarial Imitation via Variational Inverse Reinforcement Learning |
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4 |
| Adversarial Reprogramming of Neural Networks |
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3 |
| Aggregated Momentum: Stability Through Passive Damping |
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3 |
| Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees |
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✅ |
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5 |
| Amortized Bayesian Meta-Learning |
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✅ |
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4 |
| An Empirical Study of Example Forgetting during Deep Neural Network Learning |
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✅ |
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5 |
| An Empirical study of Binary Neural Networks' Optimisation |
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4 |
| An analytic theory of generalization dynamics and transfer learning in deep linear networks |
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0 |
| Analysing Mathematical Reasoning Abilities of Neural Models |
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4 |
| Analysis of Quantized Models |
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4 |
| Analyzing Inverse Problems with Invertible Neural Networks |
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1 |
| AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks |
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3 |
| Approximability of Discriminators Implies Diversity in GANs |
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3 |
| Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet |
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4 |
| Are adversarial examples inevitable? |
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3 |
| Attention, Learn to Solve Routing Problems! |
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5 |
| Attentive Neural Processes |
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4 |
| Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation |
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3 |
| AutoLoss: Learning Discrete Schedule for Alternate Optimization |
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4 |
| Automatically Composing Representation Transformations as a Means for Generalization |
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4 |
| Auxiliary Variational MCMC |
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6 |
| BA-Net: Dense Bundle Adjustment Networks |
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2 |
| BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning |
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3 |
| Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity |
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✅ |
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3 |
| Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes |
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3 |
| Bayesian Policy Optimization for Model Uncertainty |
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3 |
| Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods |
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3 |
| Benchmarking Neural Network Robustness to Common Corruptions and Perturbations |
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4 |
| Beyond Greedy Ranking: Slate Optimization via List-CVAE |
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✅ |
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2 |
| Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer |
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✅ |
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5 |
| Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers |
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4 |
| Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition |
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4 |
| Biologically-Plausible Learning Algorithms Can Scale to Large Datasets |
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✅ |
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5 |
| Boosting Robustness Certification of Neural Networks |
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5 |
| Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces |
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✅ |
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❌ |
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4 |
| Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension |
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4 |
| CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild |
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4 |
| CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model |
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✅ |
✅ |
❌ |
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4 |
| CEM-RL: Combining evolutionary and gradient-based methods for policy search |
✅ |
✅ |
✅ |
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❌ |
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4 |
| Capsule Graph Neural Network |
✅ |
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4 |
| Caveats for information bottleneck in deterministic scenarios |
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✅ |
✅ |
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4 |
| Characterizing Audio Adversarial Examples Using Temporal Dependency |
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2 |
| ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech |
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3 |
| Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering |
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❌ |
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3 |
| Combinatorial Attacks on Binarized Neural Networks |
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4 |
| Competitive experience replay |
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3 |
| Complement Objective Training |
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6 |
| Composing Complex Skills by Learning Transition Policies |
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3 |
| Conditional Network Embeddings |
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5 |
| Context-adaptive Entropy Model for End-to-end Optimized Image Compression |
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4 |
| Contingency-Aware Exploration in Reinforcement Learning |
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3 |
| Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation |
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❌ |
✅ |
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2 |
| Cost-Sensitive Robustness against Adversarial Examples |
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4 |
| Critical Learning Periods in Deep Networks |
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❌ |
✅ |
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2 |
| DARTS: Differentiable Architecture Search |
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✅ |
✅ |
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❌ |
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6 |
| DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS |
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3 |
| DHER: Hindsight Experience Replay for Dynamic Goals |
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4 |
| DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS |
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3 |
| DOM-Q-NET: Grounded RL on Structured Language |
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4 |
| DPSNet: End-to-end Deep Plane Sweep Stereo |
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3 |
| Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds |
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4 |
| Decoupled Weight Decay Regularization |
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4 |
| Deep Anomaly Detection with Outlier Exposure |
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4 |
| Deep Convolutional Networks as shallow Gaussian Processes |
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6 |
| Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks |
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3 |
| Deep Frank-Wolfe For Neural Network Optimization |
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6 |
| Deep Graph Infomax |
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4 |
| Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning |
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2 |
| Deep Layers as Stochastic Solvers |
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3 |
| Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution |
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2 |
| Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL |
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2 |
| Deep learning generalizes because the parameter-function map is biased towards simple functions |
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2 |
| Deep reinforcement learning with relational inductive biases |
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1 |
| Deep, Skinny Neural Networks are not Universal Approximators |
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1 |
| DeepOBS: A Deep Learning Optimizer Benchmark Suite |
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5 |
| Defensive Quantization: When Efficiency Meets Robustness |
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3 |
| Detecting Egregious Responses in Neural Sequence-to-sequence Models |
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4 |
| Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience |
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❌ |
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2 |
| Deterministic Variational Inference for Robust Bayesian Neural Networks |
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4 |
| Diagnosing and Enhancing VAE Models |
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2 |
| DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder |
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5 |
| Differentiable Learning-to-Normalize via Switchable Normalization |
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5 |
| Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder |
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5 |
| Diffusion Scattering Transforms on Graphs |
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1 |
| Dimensionality Reduction for Representing the Knowledge of Probabilistic Models |
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5 |
| Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information |
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2 |
| Discovery of Natural Language Concepts in Individual Units of CNNs |
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4 |
| Discriminator Rejection Sampling |
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4 |
| Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning |
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4 |
| Disjoint Mapping Network for Cross-modal Matching of Voices and Faces |
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4 |
| Distribution-Interpolation Trade off in Generative Models |
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2 |
| Diversity and Depth in Per-Example Routing Models |
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3 |
| Diversity is All You Need: Learning Skills without a Reward Function |
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4 |
| Diversity-Sensitive Conditional Generative Adversarial Networks |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Do Deep Generative Models Know What They Don't Know? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Don't let your Discriminator be fooled |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DyRep: Learning Representations over Dynamic Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Channel Pruning: Feature Boosting and Suppression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dynamic Sparse Graph for Efficient Deep Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Augmentation via Data Subsampling |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Efficient Lifelong Learning with A-GEM |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Training on Very Large Corpora via Gramian Estimation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficiently testing local optimality and escaping saddles for ReLU networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Eidetic 3D LSTM: A Model for Video Prediction and Beyond |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Emergent Coordination Through Competition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Environment Probing Interaction Policies |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Episodic Curiosity through Reachability |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Equi-normalization of Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Evaluating Robustness of Neural Networks with Mixed Integer Programming |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Excessive Invariance Causes Adversarial Vulnerability |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Execution-Guided Neural Program Synthesis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Explaining Image Classifiers by Counterfactual Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploration by random network distillation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Feature Intertwiner for Object Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Feature-Wise Bias Amplification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fixup Initialization: Residual Learning Without Normalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| FlowQA: Grasping Flow in History for Conversational Machine Comprehension |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fluctuation-dissipation relations for stochastic gradient descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Function Space Particle Optimization for Bayesian Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| GAN Dissection: Visualizing and Understanding Generative Adversarial Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| GANSynth: Adversarial Neural Audio Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GO Gradient for Expectation-Based Objectives |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GamePad: A Learning Environment for Theorem Proving |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalizable Adversarial Training via Spectral Normalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generalized Tensor Models for Recurrent Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generating Liquid Simulations with Deformation-aware Neural Networks |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Generating Multi-Agent Trajectories using Programmatic Weak Supervision |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generating Multiple Objects at Spatially Distinct Locations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generative Code Modeling with Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Generative Question Answering: Learning to Answer the Whole Question |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generative predecessor models for sample-efficient imitation learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Global-to-local Memory Pointer Networks for Task-Oriented Dialogue |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient Descent Provably Optimizes Over-parameterized Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Gradient descent aligns the layers of deep linear networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graph HyperNetworks for Neural Architecture Search |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Graph Wavelet Neural Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Guiding Policies with Language via Meta-Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Harmonic Unpaired Image-to-image Translation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Generative Modeling for Controllable Speech Synthesis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Visuomotor Control of Humanoids |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Hierarchical interpretations for neural network predictions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Hindsight policy gradients |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| How Important is a Neuron |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| How Powerful are Graph Neural Networks? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| How to train your MAML |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Human-level Protein Localization with Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hyperbolic Attention Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| INVASE: Instance-wise Variable Selection using Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Identifying and Controlling Important Neurons in Neural Machine Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Generalization and Stability of Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving MMD-GAN Training with Repulsive Loss Function |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Sequence-to-Sequence Learning via Optimal Transport |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving the Generalization of Adversarial Training with Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| InfoBot: Transfer and Exploration via the Information Bottleneck |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Information Theoretic lower bounds on negative log likelihood |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Information asymmetry in KL-regularized RL |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Information-Directed Exploration for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Initialized Equilibrium Propagation for Backprop-Free Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| InstaGAN: Instance-aware Image-to-Image Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Integer Networks for Data Compression with Latent-Variable Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Interpolation-Prediction Networks for Irregularly Sampled Time Series |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Invariant and Equivariant Graph Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Kernel Change-point Detection with Auxiliary Deep Generative Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Kernel RNN Learning (KeRNL) |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Knowledge Flow: Improve Upon Your Teachers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| L2-Nonexpansive Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Label super-resolution networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Lagging Inference Networks and Posterior Collapse in Variational Autoencoders |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| LanczosNet: Multi-Scale Deep Graph Convolutional Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Large Scale GAN Training for High Fidelity Natural Image Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Scale Graph Learning From Smooth Signals |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Large-Scale Study of Curiosity-Driven Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Latent Convolutional Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learnable Embedding Space for Efficient Neural Architecture Compression |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Actionable Representations with Goal Conditioned Policies |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning Embeddings into Entropic Wasserstein Spaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Exploration Policies for Navigation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Factorized Multimodal Representations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Finite State Representations of Recurrent Policy Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Learning Implicitly Recurrent CNNs Through Parameter Sharing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Localized Generative Models for 3D Point Clouds via Graph Convolution |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Mixed-Curvature Representations in Product Spaces |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Multi-Level Hierarchies with Hindsight |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Multimodal Graph-to-Graph Translation for Molecule Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Neural PDE Solvers with Convergence Guarantees |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Programmatically Structured Representations with Perceptor Gradients |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Protein Structure with a Differentiable Simulator |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Recurrent Binary/Ternary Weights |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Representations of Sets through Optimized Permutations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Robust Representations by Projecting Superficial Statistics Out |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Self-Imitating Diverse Policies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning To Simulate |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Two-layer Neural Networks with Symmetric Inputs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning a Meta-Solver for Syntax-Guided Program Synthesis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning a SAT Solver from Single-Bit Supervision |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning concise representations for regression by evolving networks of trees |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning deep representations by mutual information estimation and maximization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning from Positive and Unlabeled Data with a Selection Bias |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning protein sequence embeddings using information from structure |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning sparse relational transition models |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Describe Scenes with Programs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Design RNA |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning to Infer and Execute 3D Shape Programs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Learn with Conditional Class Dependencies |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Make Analogies by Contrasting Abstract Relational Structure |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Navigate the Web |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Remember More with Less Memorization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Represent Edits |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Schedule Communication in Multi-agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Understand Goal Specifications by Modelling Reward |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning what and where to attend |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning what you can do before doing anything |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning-Based Frequency Estimation Algorithms |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Local SGD Converges Fast and Communicates Little |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MARGINALIZED AVERAGE ATTENTIONAL NETWORK FOR WEAKLY-SUPERVISED LEARNING |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| M^3RL: Mind-aware Multi-agent Management Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Measuring Compositionality in Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Measuring and regularizing networks in function space |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Meta-Learning For Stochastic Gradient MCMC |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-Learning Probabilistic Inference for Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta-Learning Update Rules for Unsupervised Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Learning with Latent Embedding Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-learning with differentiable closed-form solvers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MisGAN: Learning from Incomplete Data with Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mode Normalization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modeling Uncertainty with Hedged Instance Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Modeling the Long Term Future in Model-Based Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Agent Dual Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Domain Adversarial Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-class classification without multi-class labels |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multilingual Neural Machine Translation With Soft Decoupled Encoding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multilingual Neural Machine Translation with Knowledge Distillation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multiple-Attribute Text Rewriting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Music Transformer: Generating Music with Long-Term Structure |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| NOODL: Provable Online Dictionary Learning and Sparse Coding |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Near-Optimal Representation Learning for Hierarchical Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural Graph Evolution: Towards Efficient Automatic Robot Design |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neural Logic Machines |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neural Probabilistic Motor Primitives for Humanoid Control |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Neural Program Repair by Jointly Learning to Localize and Repair |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Neural Speed Reading with Structural-Jump-LSTM |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural TTS Stylization with Adversarial and Collaborative Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Neural network gradient-based learning of black-box function interfaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| No Training Required: Exploring Random Encoders for Sentence Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Computation and Generalization of Generative Adversarial Networks under Spectrum Control |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Random Deep Weight-Tied Autoencoders: Exact Asymptotic Analysis, Phase Transitions, and Implications to Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Self Modulation for Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Sensitivity of Adversarial Robustness to Input Data Distributions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Turing Completeness of Modern Neural Network Architectures |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the loss landscape of a class of deep neural networks with no bad local valleys |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimal Completion Distillation for Sequence Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal Control Via Neural Networks: A Convex Approach |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pay Less Attention with Lightweight and Dynamic Convolutions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Phase-Aware Speech Enhancement with Deep Complex U-Net |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Poincare Glove: Hyperbolic Word Embeddings |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Policy Transfer with Strategy Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Posterior Attention Models for Sequence to Sequence Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Practical lossless compression with latent variables using bits back coding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Preconditioner on Matrix Lie Group for SGD |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Predict then Propagate: Graph Neural Networks meet Personalized PageRank |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Predicting the Generalization Gap in Deep Networks with Margin Distributions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Preferences Implicit in the State of the World |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Preventing Posterior Collapse with delta-VAEs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ProMP: Proximal Meta-Policy Search |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probabilistic Planning with Sequential Monte Carlo methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ProxQuant: Quantized Neural Networks via Proximal Operators |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Quasi-hyperbolic momentum and Adam for deep learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Quaternion Recurrent Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RNNs implicitly implement tensor-product representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Random mesh projectors for inverse problems |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Reasoning About Physical Interactions with Object-Oriented Prediction and Planning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recall Traces: Backtracking Models for Efficient Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recurrent Experience Replay in Distributed Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularized Learning for Domain Adaptation under Label Shifts |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RelGAN: Relational Generative Adversarial Networks for Text Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Relational Forward Models for Multi-Agent Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Relaxed Quantization for Discretized Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Representation Degeneration Problem in Training Natural Language Generation Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| Residual Non-local Attention Networks for Image Restoration |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Rethinking the Value of Network Pruning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revealing interpretable object representations from human behavior |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Reward Constrained Policy Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Riemannian Adaptive Optimization Methods |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Conditional Generative Adversarial Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robustness May Be at Odds with Accuracy |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SGD Converges to Global Minimum in Deep Learning via Star-convex Path |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SNAS: stochastic neural architecture search |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SOM-VAE: Interpretable Discrete Representation Learning on Time Series |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| SPIGAN: Privileged Adversarial Learning from Simulation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| STCN: Stochastic Temporal Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sample Efficient Adaptive Text-to-Speech |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sample Efficient Imitation Learning for Continuous Control |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalable Unbalanced Optimal Transport using Generative Adversarial Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Monitoring Navigation Agent via Auxiliary Progress Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Selfless Sequential Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sliced Wasserstein Auto-Encoders |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Slimmable Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Small nonlinearities in activation functions create bad local minima in neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Smoothing the Geometry of Probabilistic Box Embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Soft Q-Learning with Mutual-Information Regularization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Solving the Rubik's Cube with Approximate Policy Iteration |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Sparse Dictionary Learning by Dynamical Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Inference Networks: Unifying Deep and Spectral Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Spherical CNNs on Unstructured Grids |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Spreading vectors for similarity search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stable Opponent Shaping in Differentiable Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stable Recurrent Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stochastic Optimization of Sorting Networks via Continuous Relaxations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Prediction of Multi-Agent Interactions from Partial Observations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| StrokeNet: A Neural Painting Environment |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Structured Adversarial Attack: Towards General Implementation and Better Interpretability |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Structured Neural Summarization |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Subgradient Descent Learns Orthogonal Dictionaries |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Supervised Community Detection with Line Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Supervised Policy Update for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Synthetic Datasets for Neural Program Synthesis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Systematic Generalization: What Is Required and Can It Be Learned? |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Temporal Difference Variational Auto-Encoder |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Deep Weight Prior |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Laplacian in RL: Learning Representations with Efficient Approximations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Limitations of Adversarial Training and the Blind-Spot Attack |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Singular Values of Convolutional Layers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Unusual Effectiveness of Averaging in GAN Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The relativistic discriminator: a key element missing from standard GAN |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The role of over-parametrization in generalization of neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Theoretical Analysis of Auto Rate-Tuning by Batch Normalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Three Mechanisms of Weight Decay Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Time-Agnostic Prediction: Predicting Predictable Video Frames |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Top-Down Neural Model For Formulae |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Toward Understanding the Impact of Staleness in Distributed Machine Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards GAN Benchmarks Which Require Generalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Metamerism via Foveated Style Transfer |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Robust, Locally Linear Deep Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Towards Understanding Regularization in Batch Normalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards the first adversarially robust neural network model on MNIST |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Transfer Learning for Sequences via Learning to Collocate |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Transferring Knowledge across Learning Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Trellis Networks for Sequence Modeling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Two-Timescale Networks for Nonlinear Value Function Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding Composition of Word Embeddings via Tensor Decomposition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Successor Features Approximators |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unsupervised Adversarial Image Reconstruction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Control Through Non-Parametric Discriminative Rewards |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Discovery of Parts, Structure, and Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Domain Adaptation for Distance Metric Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Hyper-alignment for Multilingual Word Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unsupervised Learning of the Set of Local Maxima |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Learning via Meta-Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Value Propagation Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variance Networks: When Expectation Does Not Meet Your Expectations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variance Reduction for Reinforcement Learning in Input-Driven Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Variational Autoencoder with Arbitrary Conditioning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Autoencoders with Jointly Optimized Latent Dependency Structure |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Bayesian Phylogenetic Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Variational Smoothing in Recurrent Neural Network Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Verification of Non-Linear Specifications for Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Visual Reasoning by Progressive Module Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Visual Semantic Navigation using Scene Priors |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Wasserstein Barycenter Model Ensembling |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| What do you learn from context? Probing for sentence structure in contextualized word representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Whitening and Coloring Batch Transform for GANs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Wizard of Wikipedia: Knowledge-Powered Conversational Agents |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| code2seq: Generating Sequences from Structured Representations of Code |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| h-detach: Modifying the LSTM Gradient Towards Better Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| signSGD via Zeroth-Order Oracle |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| signSGD with Majority Vote is Communication Efficient and Fault Tolerant |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |