| A Bayesian Perspective on Generalization and Stochastic Gradient Descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| A DIRT-T Approach to Unsupervised Domain Adaptation |
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✅ |
✅ |
✅ |
❌ |
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4 |
| A Deep Reinforced Model for Abstractive Summarization |
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✅ |
✅ |
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3 |
| A Framework for the Quantitative Evaluation of Disentangled Representations |
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✅ |
✅ |
✅ |
❌ |
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4 |
| A Hierarchical Model for Device Placement |
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✅ |
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✅ |
✅ |
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4 |
| A Neural Representation of Sketch Drawings |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| A New Method of Region Embedding for Text Classification |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Scalable Laplace Approximation for Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| A Simple Neural Attentive Meta-Learner |
✅ |
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✅ |
✅ |
❌ |
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✅ |
4 |
| Action-dependent Control Variates for Policy Optimization via Stein Identity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Activation Maximization Generative Adversarial Nets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active Learning for Convolutional Neural Networks: A Core-Set Approach |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Active Neural Localization |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Adaptive Dropout with Rademacher Complexity Regularization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Adaptive Quantization of Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adversarial Dropout Regularization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| All-but-the-Top: Simple and Effective Postprocessing for Word Representations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Alternating Multi-bit Quantization for Recurrent Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| AmbientGAN: Generative models from lossy measurements |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Online Learning Approach to Generative Adversarial Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An efficient framework for learning sentence representations |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| An image representation based convolutional network for DNA classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Ask the Right Questions: Active Question Reformulation with Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Attacking Binarized Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Auto-Encoding Sequential Monte Carlo |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Automatically Inferring Data Quality for Spatiotemporal Forecasting |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Backpropagation through the Void: Optimizing control variates for black-box gradient estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Boosting the Actor with Dual Critic |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Boundary Seeking GANs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Breaking the Softmax Bottleneck: A High-Rank RNN Language Model |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Can Neural Networks Understand Logical Entailment? |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
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2 |
| Can recurrent neural networks warp time? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Cascade Adversarial Machine Learning Regularized with a Unified Embedding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Certified Defenses against Adversarial Examples |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Certifying Some Distributional Robustness with Principled Adversarial Training |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Communication Algorithms via Deep Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compositional Attention Networks for Machine Reasoning |
❌ |
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✅ |
✅ |
✅ |
❌ |
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4 |
| Compositional Obverter Communication Learning from Raw Visual Input |
✅ |
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❌ |
❌ |
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2 |
| Compressing Word Embeddings via Deep Compositional Code Learning |
❌ |
✅ |
✅ |
❌ |
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3 |
| Consequentialist conditional cooperation in social dilemmas with imperfect information |
✅ |
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❌ |
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2 |
| Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments |
✅ |
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❌ |
❌ |
❌ |
❌ |
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2 |
| Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Countering Adversarial Images using Input Transformations |
❌ |
✅ |
✅ |
❌ |
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3 |
| Critical Percolation as a Framework to Analyze the Training of Deep Networks |
❌ |
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❌ |
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❌ |
✅ |
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2 |
| Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| DCN+: Mixed Objective And Deep Residual Coattention for Question Answering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| DORA The Explorer: Directed Outreaching Reinforcement Action-Selection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Decision Boundary Analysis of Adversarial Examples |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Decoupling the Layers in Residual Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Deep Active Learning for Named Entity Recognition |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Complex Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Learning as a Mixed Convex-Combinatorial Optimization Problem |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Deep Learning with Logged Bandit Feedback |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Deep Neural Networks as Gaussian Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Deep Rewiring: Training very sparse deep networks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
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6 |
| Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks |
✅ |
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✅ |
❌ |
✅ |
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4 |
| Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Deep contextualized word representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Demystifying MMD GANs |
❌ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Depthwise Separable Convolutions for Neural Machine Translation |
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3 |
| Detecting Statistical Interactions from Neural Network Weights |
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✅ |
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4 |
| Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting |
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✅ |
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4 |
| Distributed Distributional Deterministic Policy Gradients |
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3 |
| Distributed Fine-tuning of Language Models on Private Data |
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3 |
| Distributed Prioritized Experience Replay |
✅ |
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4 |
| Divide and Conquer Networks |
❌ |
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❌ |
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3 |
| Divide-and-Conquer Reinforcement Learning |
✅ |
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2 |
| Do GANs learn the distribution? Some Theory and Empirics |
❌ |
❌ |
✅ |
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❌ |
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2 |
| Don't Decay the Learning Rate, Increase the Batch Size |
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❌ |
✅ |
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3 |
| Dynamic Neural Program Embeddings for Program Repair |
✅ |
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❌ |
✅ |
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❌ |
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3 |
| Efficient Sparse-Winograd Convolutional Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Eigenoption Discovery through the Deep Successor Representation |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input |
❌ |
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✅ |
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3 |
| Emergence of grid-like representations by training recurrent neural networks to perform spatial localization |
❌ |
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❌ |
❌ |
❌ |
❌ |
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1 |
| Emergent Communication in a Multi-Modal, Multi-Step Referential Game |
❌ |
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✅ |
❌ |
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4 |
| Emergent Communication through Negotiation |
❌ |
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❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Emergent Complexity via Multi-Agent Competition |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Emergent Translation in Multi-Agent Communication |
❌ |
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✅ |
✅ |
❌ |
❌ |
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3 |
| Empirical Risk Landscape Analysis for Understanding Deep Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Ensemble Adversarial Training: Attacks and Defenses |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Espresso: Efficient Forward Propagation for Binary Deep Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
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4 |
| Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Expressive power of recurrent neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| FearNet: Brain-Inspired Model for Incremental Learning |
✅ |
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✅ |
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❌ |
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3 |
| Few-Shot Learning with Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Fidelity-Weighted Learning |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Fix your classifier: the marginal value of training the last weight layer |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Fraternal Dropout |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gaussian Process Behaviour in Wide Deep Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalizing Across Domains via Cross-Gradient Training |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generalizing Hamiltonian Monte Carlo with Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generating Natural Adversarial Examples |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generating Wikipedia by Summarizing Long Sequences |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Generative Models of Visually Grounded Imagination |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Generative networks as inverse problems with Scattering transforms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Global Optimality Conditions for Deep Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient Estimators for Implicit Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Attention Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Guide Actor-Critic for Continuous Control |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| HexaConv |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hierarchical Density Order Embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Hierarchical Representations for Efficient Architecture Search |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Subtask Discovery with Non-Negative Matrix Factorization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hyperparameter optimization: a spectral approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Identifying Analogies Across Domains |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Imitation Learning from Visual Data with Multiple Intentions |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Implicit Causal Models for Genome-wide Association Studies |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving GAN Training via Binarized Representation Entropy (BRE) Regularization |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Improving GANs Using Optimal Transport |
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❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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2 |
| Initialization matters: Orthogonal Predictive State Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Interactive Grounded Language Acquisition and Generalization in a 2D World |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Interpretable Counting for Visual Question Answering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Kernel Implicit Variational Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Kronecker-factored Curvature Approximations for Recurrent Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large Scale Optimal Transport and Mapping Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large scale distributed neural network training through online distillation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Latent Space Oddity: on the Curvature of Deep Generative Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Learn to Pay Attention |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Approximate Inference Networks for Structured Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Awareness Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Deep Mean Field Games for Modeling Large Population Behavior |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Differentially Private Recurrent Language Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Discrete Weights Using the Local Reparameterization Trick |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning From Noisy Singly-labeled Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Intrinsic Sparse Structures within Long Short-Term Memory |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning Latent Permutations with Gumbel-Sinkhorn Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning One-hidden-layer Neural Networks with Landscape Design |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Parametric Closed-Loop Policies for Markov Potential Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Robust Rewards with Adverserial Inverse Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Sparse Latent Representations with the Deep Copula Information Bottleneck |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Sparse Neural Networks through L_0 Regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Wasserstein Embeddings |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning a Generative Model for Validity in Complex Discrete Structures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning a neural response metric for retinal prosthesis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning an Embedding Space for Transferable Robot Skills |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning from Between-class Examples for Deep Sound Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Learning how to explain neural networks: PatternNet and PatternAttribution |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Count Objects in Natural Images for Visual Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Multi-Task by Active Sampling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Represent Programs with Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Teach |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Learning to cluster in order to transfer across domains and tasks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Lifelong Learning with Dynamically Expandable Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Loss-aware Weight Quantization of Deep Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| META LEARNING SHARED HIERARCHIES |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| MGAN: Training Generative Adversarial Nets with Multiple Generators |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MaskGAN: Better Text Generation via Filling in the _______ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matrix capsules with EM routing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Maximum a Posteriori Policy Optimisation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Measuring the Intrinsic Dimension of Objective Landscapes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Memory Architectures in Recurrent Neural Network Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Memory Augmented Control Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Memory-based Parameter Adaptation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Meta-Learning for Semi-Supervised Few-Shot Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mitigating Adversarial Effects Through Randomization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mixed Precision Training |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Mixed Precision Training of Convolutional Neural Networks using Integer Operations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Model compression via distillation and quantization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Model-Ensemble Trust-Region Policy Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Modular Continual Learning in a Unified Visual Environment |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Monotonic Chunkwise Attention |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Mention Learning for Reading Comprehension with Neural Cascades |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multi-Scale Dense Networks for Resource Efficient Image Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Task Learning for Document Ranking and Query Suggestion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-View Data Generation Without View Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-level Residual Networks from Dynamical Systems View |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Natural Language Inference over Interaction Space |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NerveNet: Learning Structured Policy with Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Language Modeling by Jointly Learning Syntax and Lexicon |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Map: Structured Memory for Deep Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Neural Sketch Learning for Conditional Program Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Speed Reading via Skim-RNN |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Noisy Networks For Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-Autoregressive Neural Machine Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Not-So-Random Features |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Unifying Deep Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Convergence of Adam and Beyond |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Discrimination-Generalization Tradeoff in GANs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Expressive Power of Overlapping Architectures of Deep Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Information Bottleneck Theory of Deep Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the State of the Art of Evaluation in Neural Language Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the importance of single directions for generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the insufficiency of existing momentum schemes for Stochastic Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On the regularization of Wasserstein GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Learning Rate Adaptation with Hypergradient Descent |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parallelizing Linear Recurrent Neural Nets Over Sequence Length |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parameter Space Noise for Exploration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parametrized Hierarchical Procedures for Neural Programming |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PixelNN: Example-based Image Synthesis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Polar Transformer Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Policy Optimization by Genetic Distillation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Progressive Growing of GANs for Improved Quality, Stability, and Variation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Proximal Backpropagation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Quantitatively Evaluating GANs With Divergences Proposed for Training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Recasting Gradient-Based Meta-Learning as Hierarchical Bayes |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Regularizing and Optimizing LSTM Language Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reinforcement Learning Algorithm Selection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Residual Connections Encourage Iterative Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robustness of Classifiers to Universal Perturbations: A Geometric Perspective |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Routing Networks: Adaptive Selection of Non-Linear Functions for Multi-Task Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SCAN: Learning Hierarchical Compositional Visual Concepts |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SEARNN: Training RNNs with global-local losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| SMASH: One-Shot Model Architecture Search through HyperNetworks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scalable Private Learning with PATE |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-ensembling for visual domain adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semantic Interpolation in Implicit Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Semantically Decomposing the Latent Spaces of Generative Adversarial Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-parametric topological memory for navigation |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sensitivity and Generalization in Neural Networks: an Empirical Study |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Simulating Action Dynamics with Neural Process Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Skip Connections Eliminate Singularities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Smooth Loss Functions for Deep Top-k Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sobolev GAN |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Spatially Transformed Adversarial Examples |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spectral Normalization for Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SpectralNet: Spectral Clustering using Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Spherical CNNs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stabilizing Adversarial Nets with Prediction Methods |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stochastic Activation Pruning for Robust Adversarial Defense |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Variational Video Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Syntax-Directed Variational Autoencoder for Structured Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Synthesizing realistic neural population activity patterns using Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Synthetic and Natural Noise Both Break Neural Machine Translation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE ON GAN |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING & IMITATION LEARNING |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Temporal Difference Models: Model-Free Deep RL for Model-Based Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Temporally Efficient Deep Learning with Spikes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The High-Dimensional Geometry of Binary Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Implicit Bias of Gradient Descent on Separable Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Kanerva Machine: A Generative Distributed Memory |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The power of deeper networks for expressing natural functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Thermometer Encoding: One Hot Way To Resist Adversarial Examples |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Deep Learning Models Resistant to Adversarial Attacks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Image Understanding from Deep Compression Without Decoding |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Towards Neural Phrase-based Machine Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Reverse-Engineering Black-Box Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Synthesizing Complex Programs From Input-Output Examples |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards better understanding of gradient-based attribution methods for Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Training GANs with Optimism |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Training and Inference with Integers in Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Training wide residual networks for deployment using a single bit for each weight |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Trust-PCL: An Off-Policy Trust Region Method for Continuous Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Twin Networks: Matching the Future for Sequence Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unbiased Online Recurrent Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Understanding Deep Neural Networks with Rectified Linear Units |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Understanding Short-Horizon Bias in Stochastic Meta-Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Understanding image motion with group representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universal Agent for Disentangling Environments and Tasks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unsupervised Cipher Cracking Using Discrete GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Machine Translation Using Monolingual Corpora Only |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Neural Machine Translation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Unsupervised Representation Learning by Predicting Image Rotations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Continual Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Variational Inference of Disentangled Latent Concepts from Unlabeled Observations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Message Passing with Structured Inference Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Variational Network Quantization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational image compression with a scale hyperprior |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| WRPN: Wide Reduced-Precision Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Wasserstein Auto-Encoders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Wavelet Pooling for Convolutional Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| When is a Convolutional Filter Easy to Learn? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Word translation without parallel data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Zero-Shot Visual Imitation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| cGANs with Projection Discriminator |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| i-RevNet: Deep Invertible Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| mixup: Beyond Empirical Risk Minimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |