ICLR 2019: open review links

1,476 reviews of ICLR 2019 conference were released in last early November. Reviewers’ comments are also available from the Open Review website for ICLR2019. However, there was no a feature for sorting by review score, and there were so many papers that I didn’t know what to read first, so I make a sorted list with review scores.

There are two scales of review score: Rating and Confidence. The Rating is the score the reviewer gives to each paper. In other words, whether to accept or reject a paper and it represent the quality of the paper. The Confidence indicates how reviewers understand the area they are reviewing. If the reviewer is confident in his/her opinion, he/her give it as a high score. The higher the numbers for both is better in most cases. However, I just calculated it as a rough job on averaging the Ratings and sorted it in decreasing order.

The meaning of Rating

  • 1: Trivial or wrong
  • 2: Strong rejection
  • 3: Clear rejection
  • 4: Ok but not good enough – rejection
  • 5: Marginally below acceptance threshold
  • 6: Marginally above acceptance threshold
  • 7: Good paper, accept
  • 8: Top 50% of accepted papers, clear accept
  • 9: Top 15% of accepted papers, strong accept
  • 10: Top 5% of accepted papers, seminal paper

The meaning of Confidence

  • 1: The reviewer’s evaluation is an educated guess
  • 2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper
  • 3: The reviewer is fairly confident that the evaluation is correct
  • 4: The reviewer is confident but not absolutely certain that the evaluation is correct
  • 5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

Note

2 to 5 reviewers per paper, but usually 3 to 4.

I sorted the papers in descending order with a Rating average. In the list below, the meaning of each value is rank, an average of all Ratings, a standard deviation of Ratings, all Rating scores/ all Confidence scores and title.

In addition, after I made the list below and Googleed it, I found a site that already ranks just like me.

However, some scores are correct on the bottom list because it’s more recent updates than the site. So I’m going to look at what I made.

If that’s helpful, look at the list below and let me know if you find any interesting papers.

  1. 8.67, 1.25, [9, 10, 7] [3, 5, 3]GENERATING HIGH FIDELITY IMAGES WITH SUBSCALE PIXEL NETWORKS AND MULTIDIMENSIONAL UPSCALING
  2. 8.33, 0.94, [9, 9, 7] [4, 5, 3]Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
  3. 8.33, 1.25, [10, 7, 8] [4, 3, 4]Large Scale GAN Training for High Fidelity Natural Image Synthesis
  4. 8.33, 1.70, [9, 6, 10] [5, 4, 5]ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA
  5. 8.00, 0.82, [7, 9, 8] [5, 5, 3]Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
  6. 8.00, 0.82, [8, 7, 9] [4, 3, 4]Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
  7. 8.00, 0.82, [7, 9, 8] [4, 5, 4]Slimmable Neural Networks
  8. 8.00, 0.00, [8, 8, 8] [4, 2, 5]Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
  9. 8.00, 0.82, [7, 9, 8] [5, 4, 4]Temporal Difference Variational Auto-Encoder
  10. 8.00, 1.63, [8, 10, 6] [3, 4, 3]Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
  11. 8.00, 0.00, [8, 8, 8] [3, 4, 3]Unsupervised Learning of the Set of Local Maxima
  12. 8.00, 0.82, [7, 8, 9] [4, 4, 5]An Empirical Study of Example Forgetting during Deep Neural Network Learning
  13. 8.00, 0.82, [7, 9, 8] [4, 4, 5]Posterior Attention Models for Sequence to Sequence Learning
  14. 7.67, 0.47, [7, 8, 8] [3, 4, 3]Smoothing the Geometry of Probabilistic Box Embeddings
  15. 7.67, 1.25, [8, 6, 9] [4, 4, 4]ON RANDOM DEEP AUTOENCODERS: EXACT ASYMPTOTIC ANALYSIS, PHASE TRANSITIONS, AND IMPLICATIONS TO TRAINING
  16. 7.67, 0.94, [9, 7, 7] [4, 2, 3]Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
  17. 7.67, 1.70, [6, 10, 7] [4, 3, 3]Identifying and Controlling Important Neurons in Neural Machine Translation
  18. 7.67, 1.25, [6, 8, 9] [5, 4, 4]Critical Learning Periods in Deep Networks
  19. 7.67, 1.25, [8, 9, 6] [4, 4, 4]Sparse Dictionary Learning by Dynamical Neural Networks
  20. 7.67, 1.70, [7, 10, 6] [4, 4, 4]KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
  21. 7.67, 0.47, [8, 7, 8] [3, 3, 4]Learning Unsupervised Learning Rules
  22. 7.67, 0.94, [9, 7, 7] [3, 4, 4]Learning Robust Representations by Projecting Superficial Statistics Out
  23. 7.67, 0.94, [9, 7, 7] [5, 5, 4]A2BCD: Asynchronous Acceleration with Optimal Complexity
  24. 7.67, 0.47, [8, 7, 8] [4, 4, 4]Pay Less Attention with Lightweight and Dynamic Convolutions
  25. 7.67, 1.25, [8, 9, 6] [4, 4, 4]Supervised Community Detection with Line Graph Neural Networks
  26. 7.67, 0.47, [8, 7, 8] [2, 4, 3]Robustness May Be at Odds with Accuracy
  27. 7.67, 0.47, [7, 8, 8] [4, 4, 3]Kernel Change-point Detection with Auxiliary Deep Generative Models
  28. 7.67, 0.47, [8, 8, 7] [4, 4, 4]Adaptive Input Representations for Neural Language Modeling
  29. 7.67, 0.47, [7, 8, 8] [4, 3, 3]A Variational Inequality Perspective on Generative Adversarial Networks
  30. 7.67, 0.94, [7, 9, 7] [4, 4, 4]Composing Complex Skills by Learning Transition Policies with Proximity Reward Induction
  31. 7.67, 0.47, [7, 8, 8] [4, 3, 4]Towards Robust, Locally Linear Deep Networks
  32. 7.50, 0.50, [7, 8, 8, 7] [4, 3, 3, 4]On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
  33. 7.50, 2.29, [7, 10, 9, 4] [4, 4, 5, 4]Exploration by random network distillation
  34. 7.33, 1.70, [9, 8, 5] [4, 3, 4]Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer
  35. 7.33, 1.25, [7, 6, 9] [3, 4, 3]Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
  36. 7.33, 0.47, [7, 7, 8] [4, 2, 3]Dynamic Sparse Graph for Efficient Deep Learning
  37. 7.33, 0.47, [8, 7, 7] [5, 4, 3]Differentiable Learning-to-Normalize via Switchable Normalization
  38. 7.33, 0.47, [7, 8, 7] [4, 4, 3]Learning to Remember More with Less Memorization
  39. 7.33, 1.25, [7, 9, 6] [3, 5, 4]Large-Scale Study of Curiosity-Driven Learning
  40. 7.33, 0.47, [7, 8, 7] [1, 5, 5]Evaluating Robustness of Neural Networks with Mixed Integer Programming
  41. 7.33, 0.47, [8, 7, 7] [4, 3, 3]Small nonlinearities in activation functions create bad local minima in neural networks
  42. 7.33, 0.47, [7, 7, 8] [3, 3, 2]Approximability of Discriminators Implies Diversity in GANs
  43. 7.33, 0.47, [7, 7, 8] [4, 3, 4]Diversity is All You Need: Learning Skills without a Reward Function
  44. 7.33, 0.47, [8, 7, 7] [4, 4, 5]Deep Frank-Wolfe For Neural Network Optimization
  45. 7.33, 1.25, [9, 7, 6] [3, 3, 3]ProMP: Proximal Meta-Policy Search
  46. 7.33, 0.47, [7, 8, 7] [2, 2, 4]Efficient Training on Very Large Corpora via Gramian Estimation
  47. 7.33, 1.25, [6, 9, 7] [5, 4, 4]Gradient descent aligns the layers of deep linear networks
  48. 7.33, 0.94, [6, 8, 8] [3, 4, 4]Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
  49. 7.33, 0.47, [7, 8, 7] [5, 4, 3]Time-Agnostic Prediction: Predicting Predictable Video Frames
  50. 7.33, 2.36, [4, 9, 9] [4, 4, 5]Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
  51. 7.33, 0.47, [7, 8, 7] [5, 4, 4]Towards Metamerism via Foveated Style Transfer
  52. 7.33, 0.47, [7, 7, 8] [5, 5, 5]Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
  53. 7.33, 0.47, [8, 7, 7] [4, 5, 3]LanczosNet: Multi-Scale Deep Graph Convolutional Networks
  54. 7.33, 0.47, [8, 7, 7] [4, 4, 3]Visualizing and Understanding Generative Adversarial Networks
  55. 7.33, 1.25, [9, 6, 7] [4, 4, 4]Label super-resolution networks
  56. 7.00, 0.82, [7, 8, 6] [4, 4, 4]Deep, Skinny Neural Networks are not Universal Approximators
  57. 7.00, 0.82, [8, 7, 6] [3, 5, 2]DARTS: Differentiable Architecture Search
  58. 7.00, 1.41, [6, 9, 6] [3, 5, 4]Diffusion Scattering Transforms on Graphs
  59. 7.00, 1.63, [7, 5, 9] [5, 3, 4]ADVERSARIAL DOMAIN ADAPTATION FOR STABLE BRAIN-MACHINE INTERFACES
  60. 7.00, 0.00, [7, 7, 7] [4, 2, 2]CoT: Cooperative Training for Generative Modeling of Discrete Data
  61. 7.00, 0.82, [6, 7, 8] [3, 4, 4]An analytic theory of generalization dynamics and transfer learning in deep linear networks
  62. 7.00, 0.00, [7, 7, 7] [5, 3, 3]Deterministic Variational Inference for Robust Bayesian Neural Networks
  63. 7.00, 0.82, [6, 7, 8] [3, 4, 4]Learning Latent Superstructures in Variational Autoencoders for Deep Multidimensional Clustering
  64. 7.00, 0.00, [7, 7, 7] [3, 4, 4]EMI: Exploration with Mutual Information Maximizing State and Action Embeddings
  65. 7.00, 0.00, [7, 7, 7] [3, 4, 3]Learning a SAT Solver from Single-Bit Supervision
  66. 7.00, 0.00, [7, 7, 7] [5, 4, 4]Generative Code Modeling with Graphs
  67. 7.00, 0.82, [8, 6, 7] [4, 2, 4]Meta-Learning Probabilistic Inference for Prediction
  68. 7.00, 0.00, [7, 7, 7] [4, 3, 4]Relaxed Quantization for Discretized Neural Networks
  69. 7.00, 0.82, [7, 8, 6] [4, 2, 4]Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
  70. 7.00, 0.00, [7, 7, 7] [4, 4, 5]Auxiliary Variational MCMC
  71. 7.00, 1.41, [9, 6, 6] [4, 4, 5]SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
  72. 7.00, 1.63, [7, 5, 9] [3, 4, 4]Deep Graph Infomax
  73. 7.00, 0.00, [7, 7, 7] [4, 5, 3]Riemannian Adaptive Optimization Methods
  74. 7.00, 0.82, [8, 7, 6] [2, 3, 4]Detecting Egregious Responses in Neural Sequence-to-sequence Models
  75. 7.00, 0.82, [7, 8, 6] [5, 5, 3]Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution
  76. 7.00, 0.00, [7, 7, 7] [5, 2, 4]How Important is a Neuron
  77. 7.00, 0.00, [7, 7, 7] [3, 3, 3]Neural network gradient-based learning of black-box function interfaces
  78. 7.00, 0.82, [8, 6, 7] [4, 5, 4]Wizard of Wikipedia: Knowledge-Powered Conversational Agents
  79. 7.00, 2.16, [9, 4, 8] [4, 5, 5]Invariant and Equivariant Graph Networks
  80. 7.00, 1.41, [8, 8, 5] [3, 5, 5]The effects of neural resource constraints on early visual representations
  81. 7.00, 0.82, [8, 6, 7] [4, 2, 3]Recurrent Experience Replay in Distributed Reinforcement Learning
  82. 7.00, 1.41, [9, 6, 6] [3, 4, 4]Padam: Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
  83. 7.00, 0.82, [7, 6, 8] [3, 4, 2]Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
  84. 7.00, 0.82, [8, 6, 7] [3, 4, 4]Quasi-hyperbolic momentum and Adam for deep learning
  85. 7.00, 0.00, [7, 7, 7] [3, 3, 3]The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure
  86. 7.00, 1.41, [8, 5, 8] [4, 2, 3]Learning Self-Imitating Diverse Policies
  87. 7.00, 1.41, [8, 5, 8] [4, 4, 5]Unsupervised Domain Adaptation for Distance Metric Learning
  88. 7.00, 0.00, [7, 7, 7] [3, 4, 4]Scalable Reversible Generative Models with Free-form Continuous Dynamics
  89. 7.00, 1.63, [7, 9, 5] [3, 4, 4]Feature Intertwiners
  90. 7.00, 0.00, [7, 7, 7] [4, 4, 4]Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching
  91. 7.00, 1.63, [7, 5, 9] [4, 3, 4]ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
  92. 7.00, 0.82, [6, 8, 7] [4, 4, 4]textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE WITH DISTRIBUTED COMPOSITIONAL PRIOR
  93. 7.00, 1.41, [8, 5, 8] [4, 5, 5]Local SGD Converges Fast and Communicates Little
  94. 7.00, 0.00, [7, 7, 7] [3, 5, 3]The role of over-parametrization in generalization of neural networks
  95. 7.00, 1.63, [9, 5, 7] [5, 4, 4]The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
  96. 7.00, 1.41, [5, 8, 8] [3, 4, 3]Don’t Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
  97. 7.00, 0.00, [7, 7, 7] [4, 4, 4]What do you learn from context? Probing for sentence structure in contextualized word representations
  98. 7.00, 0.82, [6, 7, 8] [4, 3, 4]Learning Implicitly Recurrent CNNs Through Parameter Sharing
  99. 7.00, 1.41, [8, 8, 5] [4, 4, 4]The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
  100. 7.00, 0.82, [6, 8, 7] [3, 4, 4]Learning Neural PDE Solvers with Convergence Guarantees
  101. 7.00, 0.82, [8, 6, 7] [4, 4, 4]Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
  102. 7.00, 0.00, [7, 7, 7] [3, 3, 3]Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL
  103. 7.00, 0.00, [7, 7, 7] [5, 3, 3]Modeling Uncertainty with Hedged Instance Embeddings
  104. 7.00, 0.00, [7, 7, 7] [3, 3, 3]Learning to Navigate the Web
  105. 7.00, 0.00, [7, 7, 7] [4, 3, 2]G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
  106. 7.00, 0.82, [8, 7, 6] [3, 4, 3]GANSynth: Adversarial Neural Audio Synthesis
  107. 7.00, 0.82, [8, 6, 7] [4, 3, 5]K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning
  108. 7.00, 0.82, [8, 7, 6] [3, 2, 4]Learning sparse relational transition models
  109. 7.00, 0.82, [8, 6, 7] [4, 3, 4]Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
  110. 7.00, 0.82, [8, 6, 7] [3, 3, 3]On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks
  111. 7.00, 1.41, [5, 8, 8] [3, 2, 2]Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
  112. 6.80, 0.40, [6, 7, 7, 7, 7] [1, 3, 4, 3, 2]Subgradient Descent Learns Orthogonal Dictionaries
  113. 6.67, 1.25, [5, 7, 8] [3, 3, 2]Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability
  114. 6.67, 0.47, [6, 7, 7] [3, 4, 2]RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
  115. 6.67, 0.94, [8, 6, 6] [3, 3, 4]Principled Deep Neural Network Training through Linear Programming
  116. 6.67, 0.94, [8, 6, 6] [4, 4, 4]Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
  117. 6.67, 0.47, [7, 7, 6] [4, 4, 4]Analysis of Quantized Models
  118. 6.67, 0.94, [8, 6, 6] [5, 3, 4]Practical lossless compression with latent variables using bits back coding
  119. 6.67, 0.47, [6, 7, 7] [5, 4, 4]Sample Efficient Adaptive Text-to-Speech
  120. 6.67, 1.25, [7, 5, 8] [3, 4, 4]Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
  121. 6.67, 1.89, [8, 4, 8] [5, 4, 4]LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
  122. 6.67, 0.47, [7, 7, 6] [3, 3, 2]Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
  123. 6.67, 0.47, [6, 7, 7] [3, 3, 4]Towards the first adversarially robust neural network model on MNIST
  124. 6.67, 0.94, [6, 8, 6] [3, 4, 4]Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy
  125. 6.67, 0.47, [6, 7, 7] [3, 4, 4]Meta-Learning For Stochastic Gradient MCMC
  126. 6.67, 0.47, [7, 6, 7] [3, 3, 3]Trellis Networks for Sequence Modeling
  127. 6.67, 0.47, [6, 7, 7] [3, 3, 4]ADef: an Iterative Algorithm to Construct Adversarial Deformations
  128. 6.67, 0.47, [7, 6, 7] [3, 2, 4]Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
  129. 6.67, 0.94, [6, 8, 6] [2, 5, 3]Learning to Schedule Communication in Multi-agent Reinforcement Learning
  130. 6.67, 0.47, [6, 7, 7] [3, 2, 3]Learning Factorized Multimodal Representations
  131. 6.67, 0.94, [8, 6, 6] [4, 5, 4]Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
  132. 6.67, 1.70, [6, 5, 9] [4, 2, 4]Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series
  133. 6.67, 0.47, [7, 6, 7] [3, 4, 3]Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network
  134. 6.67, 0.47, [7, 7, 6] [4, 5, 5]Hyperbolic Attention Networks
  135. 6.67, 1.25, [7, 5, 8] [3, 3, 3]NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
  136. 6.67, 0.47, [7, 7, 6] [4, 2, 3]Latent Convolutional Models
  137. 6.67, 1.25, [8, 7, 5] [4, 4, 3]Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
  138. 6.67, 0.47, [7, 7, 6] [4, 3, 4]Generalized Tensor Models for Recurrent Neural Networks
  139. 6.67, 0.94, [6, 8, 6] [2, 4, 4]Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
  140. 6.67, 0.94, [8, 6, 6] [3, 4, 4]Episodic Curiosity through Reachability
  141. 6.67, 0.47, [6, 7, 7] [3, 4, 4]Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
  142. 6.67, 0.47, [7, 6, 7] [3, 4, 4]Solving the Rubik’s Cube with Approximate Policy Iteration
  143. 6.67, 0.47, [7, 6, 7] [4, 5, 3]Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
  144. 6.67, 0.94, [6, 8, 6] [3, 4, 3]Unsupervised Learning via Meta-Learning
  145. 6.67, 0.94, [6, 6, 8] [2, 2, 2]Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
  146. 6.67, 0.94, [6, 6, 8] [3, 2, 4]A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
  147. 6.67, 0.47, [7, 6, 7] [4, 5, 4]Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
  148. 6.67, 1.25, [8, 5, 7] [4, 4, 4]No Training Required: Exploring Random Encoders for Sentence Classification
  149. 6.67, 0.47, [7, 6, 7] [5, 4, 4]Learning Two-layer Neural Networks with Symmetric Inputs
  150. 6.67, 0.47, [7, 7, 6] [4, 4, 4]Phase-Aware Speech Enhancement with Deep Complex U-Net
  151. 6.67, 1.25, [8, 5, 7] [1, 5, 4]Deep Layers as Stochastic Solvers
  152. 6.67, 0.47, [7, 6, 7] [4, 4, 4]Graph HyperNetworks for Neural Architecture Search
  153. 6.67, 1.25, [7, 8, 5] [4, 4, 4]Complement Objective Training
  154. 6.67, 0.47, [7, 6, 7] [4, 4, 3]Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
  155. 6.67, 0.94, [8, 6, 6] [4, 4, 4]Generative Question Answering: Learning to Answer the Whole Question
  156. 6.67, 1.70, [6, 9, 5] [3, 4, 4]Detecting Adversarial Examples Via Neural Fingerprinting
  157. 6.67, 1.25, [8, 5, 7] [4, 3, 1]Learning concise representations for regression by evolving networks of trees
  158. 6.67, 0.47, [6, 7, 7] [3, 4, 4]Optimal Completion Distillation for Sequence Learning
  159. 6.67, 1.70, [9, 5, 6] [4, 4, 4]AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
  160. 6.67, 0.47, [7, 6, 7] [4, 3, 1]Visual Semantic Navigation using Scene Priors
  161. 6.67, 0.47, [7, 7, 6] [3, 5, 4]Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
  162. 6.67, 0.47, [6, 7, 7] [4, 5, 4]A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
  163. 6.67, 0.47, [6, 7, 7] [4, 4, 4]Adversarial Attacks on Graph Neural Networks via Meta Learning
  164. 6.67, 0.47, [6, 7, 7] [4, 5, 3]Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
  165. 6.67, 0.47, [7, 7, 6] [4, 4, 4]Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
  166. 6.67, 0.47, [7, 7, 6] [5, 4, 4]Three Mechanisms of Weight Decay Regularization
  167. 6.67, 0.47, [6, 7, 7] [4, 2, 2]Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
  168. 6.67, 0.94, [6, 8, 6] [3, 4, 4]Transferring Knowledge across Learning Processes
  169. 6.67, 1.70, [9, 5, 6] [3, 4, 1]Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
  170. 6.67, 0.47, [7, 6, 7] [2, 3, 4]Defensive Quantization: When Efficiency Meets Robustness
  171. 6.67, 1.25, [7, 8, 5] [3, 4, 5]Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
  172. 6.67, 0.47, [7, 6, 7] [4, 4, 5]FlowQA: Grasping Flow in History for Conversational Machine Comprehension
  173. 6.67, 0.94, [6, 6, 8] [4, 5, 4]Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion
  174. 6.67, 1.25, [8, 5, 7] [4, 2, 1]GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
  175. 6.67, 0.47, [7, 7, 6] [3, 3, 2]Automatically Composing Representation Transformations as a Means for Generalization
  176. 6.67, 0.47, [6, 7, 7] [4, 4, 3]RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
  177. 6.67, 1.25, [8, 7, 5] [4, 4, 3]Looking for ELMo’s friends: Sentence-Level Pretraining Beyond Language Modeling
  178. 6.67, 0.47, [7, 6, 7] [3, 1, 3]A Mean Field Theory of Batch Normalization
  179. 6.67, 1.25, [5, 7, 8] [3, 3, 4]Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
  180. 6.67, 0.47, [7, 6, 7] [4, 3, 4]Active Learning with Partial Feedback
  181. 6.67, 0.47, [7, 6, 7] [4, 5, 4]Learning from Incomplete Data with Generative Adversarial Networks
  182. 6.67, 0.47, [7, 6, 7] [3, 4, 4]Do Deep Generative Models Know What They Don’t Know?
  183. 6.67, 0.94, [6, 8, 6] [4, 4, 4]RelGAN: Relational Generative Adversarial Networks for Text Generation
  184. 6.67, 0.47, [7, 6, 7] [2, 2, 2]Provable Online Dictionary Learning and Sparse Coding
  185. 6.67, 0.94, [6, 6, 8] [4, 4, 4]Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
  186. 6.67, 0.47, [7, 7, 6] [4, 5, 5]SPIGAN: Privileged Adversarial Learning from Simulation
  187. 6.67, 0.47, [7, 6, 7] [4, 3, 4]Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
  188. 6.67, 0.47, [7, 7, 6] [4, 5, 4]Learning to Infer and Execute 3D Shape Programs
  189. 6.67, 1.89, [8, 4, 8] [4, 4, 4]A Generative Model For Electron Paths
  190. 6.67, 0.94, [6, 6, 8] [3, 2, 4]Stochastic Optimization of Sorting Networks via Continuous Relaxations
  191. 6.67, 0.47, [7, 6, 7] [2, 5, 4]Learning a Meta-Solver for Syntax-Guided Program Synthesis
  192. 6.67, 0.94, [6, 8, 6] [1, 4, 4]There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
  193. 6.67, 1.25, [7, 8, 5] [5, 4, 5]Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference
  194. 6.50, 1.50, [4, 7, 7, 8] [3, 3, 2, 5]Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
  195. 6.33, 0.94, [7, 5, 7] [3, 5, 3]Stochastic Gradient Descent Learns State Equations with Nonlinear Activations
  196. 6.33, 0.47, [6, 6, 7] [4, 3, 3]Improved Gradient Estimators for Stochastic Discrete Variables
  197. 6.33, 1.70, [7, 4, 8] [3, 5, 5]Learning Preconditioner on Matrix Lie Group
  198. 6.33, 1.25, [8, 5, 6] [4, 4, 4]Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator
  199. 6.33, 0.47, [7, 6, 6] [5, 4, 2]Local Critic Training of Deep Neural Networks
  200. 6.33, 1.70, [4, 8, 7] [4, 4, 4]Are adversarial examples inevitable?
  201. 6.33, 1.25, [6, 8, 5] [4, 4, 4]Generating Multiple Objects at Spatially Distinct Locations
  202. 6.33, 0.47, [6, 6, 7] [4, 4, 3]DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS
  203. 6.33, 1.25, [6, 5, 8] [2, 5, 3]signSGD via Zeroth-Order Oracle
  204. 6.33, 0.47, [6, 7, 6] [2, 2, 4]Reward Constrained Policy Optimization
  205. 6.33, 1.25, [5, 6, 8] [5, 5, 4]Quaternion Recurrent Neural Networks
  206. 6.33, 0.94, [5, 7, 7] [3, 3, 4]DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
  207. 6.33, 1.89, [5, 5, 9] [4, 3, 5]Laplacian Networks: Bounding Indicator Function Smoothness for Neural Networks Robustness
  208. 6.33, 0.94, [5, 7, 7] [4, 4, 5]Why do deep convolutional networks generalize so poorly to small image transformations?
  209. 6.33, 1.25, [6, 8, 5] [4, 3, 3]Hierarchical Visuomotor Control of Humanoids
  210. 6.33, 0.94, [7, 5, 7] [4, 4, 4]Hindsight policy gradients
  211. 6.33, 0.47, [7, 6, 6] [4, 4, 4]Attentive Neural Processes
  212. 6.33, 0.94, [7, 5, 7] [5, 5, 4]ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
  213. 6.33, 0.94, [7, 5, 7] [5, 4, 2]Execution-Guided Neural Program Synthesis
  214. 6.33, 0.47, [7, 6, 6] [4, 5, 3]Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration
  215. 6.33, 1.25, [5, 8, 6] [4, 3, 3]Learning Recurrent Binary/Ternary Weights
  216. 6.33, 0.47, [7, 6, 6] [5, 5, 5]Attention, Learn to Solve Routing Problems!
  217. 6.33, 0.94, [5, 7, 7] [4, 3, 3]Improving Generalization and Stability of Generative Adversarial Networks
  218. 6.33, 0.47, [7, 6, 6] [5, 4, 4]Visceral Machines: Reinforcement Learning with Intrinsic Physiological Rewards
  219. 6.33, 0.47, [6, 6, 7] [4, 3, 3]Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications
  220. 6.33, 0.94, [5, 7, 7] [4, 2, 3]L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data
  221. 6.33, 0.94, [7, 7, 5] [4, 3, 4]Deep reinforcement learning with relational inductive biases
  222. 6.33, 0.47, [6, 7, 6] [4, 4, 4]GO Gradient for Expectation-Based Objectives
  223. 6.33, 0.94, [7, 5, 7] [3, 4, 4]PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
  224. 6.33, 0.94, [7, 5, 7] [2, 3, 4]Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
  225. 6.33, 1.25, [6, 8, 5] [5, 5, 4]Multi-Domain Adversarial Learning
  226. 6.33, 0.47, [6, 7, 6] [5, 5, 2]Improving MMD-GAN Training with Repulsive Loss Function
  227. 6.33, 0.47, [6, 6, 7] [4, 3, 4]FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS
  228. 6.33, 1.25, [5, 6, 8] [4, 4, 4]Autoencoder-based Music Translation
  229. 6.33, 1.25, [6, 5, 8] [3, 4, 5]Fluctuation-dissipation relations for stochastic gradient descent
  230. 6.33, 0.47, [7, 6, 6] [3, 4, 4]Adaptive Estimators Show Information Compression in Deep Neural Networks
  231. 6.33, 1.25, [5, 8, 6] [5, 4, 4]On the loss landscape of a class of deep neural networks with no bad local valleys
  232. 6.33, 0.47, [6, 7, 6] [4, 4, 3]Multilingual Neural Machine Translation with Knowledge Distillation
  233. 6.33, 0.94, [5, 7, 7] [3, 3, 3]Emergent Coordination Through Competition
  234. 6.33, 1.70, [7, 8, 4] [4, 5, 3]Knowledge Flow: Improve Upon Your Teachers
  235. 6.33, 0.94, [5, 7, 7] [4, 3, 3]Representation Degeneration Problem in Training Natural Language Generation Models
  236. 6.33, 0.47, [6, 7, 6] [4, 4, 4]SNAS: stochastic neural architecture search
  237. 6.33, 0.47, [6, 6, 7] [3, 4, 2]Understanding Composition of Word Embeddings via Tensor Decomposition
  238. 6.33, 1.25, [6, 5, 8] [4, 4, 5]Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
  239. 6.33, 0.47, [6, 6, 7] [4, 4, 4]RNNs implicitly implement tensor-product representations
  240. 6.33, 0.94, [5, 7, 7] [2, 3, 2]STRUCTURED ADVERSARIAL ATTACK: TOWARDS GENERAL IMPLEMENTATION AND BETTER INTERPRETABILITY
  241. 6.33, 0.94, [7, 7, 5] [3, 4, 5]Learning deep representations by mutual information estimation and maximization
  242. 6.33, 0.47, [7, 6, 6] [3, 4, 3]Bayesian Policy Optimization for Model Uncertainty
  243. 6.33, 1.25, [6, 8, 5] [3, 5, 3]A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS
  244. 6.33, 0.47, [7, 6, 6] [5, 4, 3]From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
  245. 6.33, 0.47, [6, 6, 7] [4, 3, 4]Discriminator Rejection Sampling
  246. 6.33, 0.47, [6, 7, 6] [5, 5, 5]AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
  247. 6.33, 0.94, [7, 5, 7] [4, 3, 3]The Laplacian in RL: Learning Representations with Efficient Approximations
  248. 6.33, 0.47, [7, 6, 6] [4, 2, 4]On Computation and Generalization of Generative Adversarial Networks under Spectrum Control
  249. 6.33, 0.47, [7, 6, 6] [5, 3, 3]Learning Finite State Representations of Recurrent Policy Networks
  250. 6.33, 0.47, [7, 6, 6] [2, 3, 5]Analyzing Inverse Problems with Invertible Neural Networks
  251. 6.33, 0.94, [7, 5, 7] [4, 5, 4]On Self Modulation for Generative Adversarial Networks
  252. 6.33, 0.94, [5, 7, 7] [5, 3, 4]Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation
  253. 6.33, 0.47, [7, 6, 6] [4, 2, 4]Universal Transformers
  254. 6.33, 0.47, [7, 6, 6] [5, 3, 4]Variational Autoencoders with Jointly Optimized Latent Dependency Structure
  255. 6.33, 1.25, [5, 6, 8] [4, 5, 4]Hierarchical Generative Modeling for Controllable Speech Synthesis
  256. 6.33, 0.47, [6, 6, 7] [3, 3, 3]Individualized Controlled Continuous Communication Model for Multiagent Cooperative and Competitive Tasks
  257. 6.33, 1.25, [5, 6, 8] [4, 2, 3]A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
  258. 6.33, 0.47, [7, 6, 6] [5, 4, 4]Instance-aware Image-to-Image Translation
  259. 6.33, 1.70, [7, 8, 4] [3, 4, 4]The Deep Weight Prior
  260. 6.33, 1.70, [8, 4, 7] [4, 4, 4]Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
  261. 6.33, 1.89, [9, 5, 5] [5, 4, 4]From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
  262. 6.33, 0.47, [6, 7, 6] [4, 4, 4]Empirical Bounds on Linear Regions of Deep Rectifier Networks
  263. 6.33, 0.47, [7, 6, 6] [4, 4, 5]Multilingual Neural Machine Translation With Soft Decoupled Encoding
  264. 6.33, 0.47, [6, 7, 6] [3, 2, 3]On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization
  265. 6.33, 0.47, [6, 6, 7] [4, 4, 5]MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
  266. 6.33, 1.70, [7, 8, 4] [3, 4, 3]CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild
  267. 6.33, 1.25, [5, 6, 8] [4, 3, 4]BNN+: Improved Binary Network Training
  268. 6.33, 1.70, [8, 7, 4] [3, 4, 5]Statistical Verification of Neural Networks
  269. 6.33, 1.25, [8, 5, 6] [4, 5, 4]Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
  270. 6.33, 0.47, [6, 6, 7] [4, 4, 2]Stable Recurrent Models
  271. 6.33, 0.94, [7, 5, 7] [3, 5, 2]Learning Mixed-Curvature Representations in Product Spaces
  272. 6.33, 0.47, [6, 6, 7] [3, 3, 3]Generating Multi-Agent Trajectories using Programmatic Weak Supervision
  273. 6.33, 0.47, [7, 6, 6] [4, 4, 4]Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
  274. 6.33, 2.05, [4, 6, 9] [4, 4, 4]BA-Net: Dense Bundle Adjustment Networks
  275. 6.33, 2.05, [4, 9, 6] [4, 4, 4]Variance Reduction for Reinforcement Learning in Input-Driven Environments
  276. 6.33, 1.89, [5, 9, 5] [4, 4, 4]Predicting the Generalization Gap in Deep Networks with Margin Distributions
  277. 6.33, 1.25, [6, 5, 8] [4, 5, 5]Unsupervised Control Through Non-Parametric Discriminative Rewards
  278. 6.33, 0.94, [7, 5, 7] [4, 5, 3]Information asymmetry in KL-regularized RL
  279. 6.33, 0.94, [7, 5, 7] [5, 5, 3]Diversity-Sensitive Conditional Generative Adversarial Networks
  280. 6.33, 0.94, [7, 5, 7] [3, 4, 3]The Unreasonable Effectiveness of (Zero) Initialization in Deep Residual Learning
  281. 6.33, 0.47, [6, 7, 6] [3, 4, 3]Preventing Posterior Collapse with delta-VAEs
  282. 6.33, 1.70, [8, 7, 4] [4, 4, 5]TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
  283. 6.33, 0.47, [6, 7, 6] [5, 4, 4]Feature-Wise Bias Amplification
  284. 6.33, 1.25, [8, 5, 6] [5, 5, 3]Machine Translation With Weakly Paired Bilingual Documents
  285. 6.33, 0.94, [5, 7, 7] [4, 3, 3]Don’t let your Discriminator be fooled
  286. 6.33, 1.89, [5, 5, 9] [4, 3, 4]Diagnosing and Enhancing VAE Models
  287. 6.33, 0.47, [7, 6, 6] [5, 3, 3]Spherical CNNs on Unstructured Grids
  288. 6.33, 2.05, [6, 9, 4] [5, 4, 5]Toward Understanding the Impact of Staleness in Distributed Machine Learning
  289. 6.33, 0.94, [7, 5, 7] [2, 4, 3]On the Sensitivity of Adversarial Robustness to Input Data Distributions
  290. 6.33, 1.89, [9, 5, 5] [4, 4, 5]Reasoning About Physical Interactions with Object-Centric Models
  291. 6.33, 0.47, [6, 6, 7] [3, 4, 3]Multiple-Attribute Text Rewriting
  292. 6.33, 1.25, [6, 8, 5] [4, 4, 4]Neural Graph Evolution: Automatic Robot Design
  293. 6.33, 0.47, [6, 7, 6] [4, 3, 4]Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
  294. 6.33, 1.25, [8, 6, 5] [4, 5, 4]DyRep: Learning Representations over Dynamic Graphs
  295. 6.33, 0.47, [6, 6, 7] [4, 4, 5]Eidetic 3D LSTM: A Model for Video Prediction and Beyond
  296. 6.33, 1.70, [7, 4, 8] [3, 3, 5]Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering
  297. 6.33, 0.94, [5, 7, 7] [4, 2, 3]The Limitations of Adversarial Training and the Blind-Spot Attack
  298. 6.33, 0.47, [6, 6, 7] [4, 4, 3]Regularized Learning for Domain Adaptation under Label Shifts
  299. 6.25, 0.83, [7, 7, 5, 6] [4, 1, 4, 4]Towards Consistent Performance on Atari using Expert Demonstrations
  300. 6.25, 0.83, [5, 7, 7, 6] [3, 3, 4, 5]Learning Protein Structure with a Differentiable Simulator
  301. 6.25, 0.83, [7, 5, 7, 6] [4, 3, 3, 4]The Implicit Preference Information in an Initial State
  302. 6.25, 0.83, [7, 6, 7, 5] [5, 4, 4, 4]Competitive experience replay
  303. 6.25, 1.09, [8, 6, 6, 5] [3, 3, 2, 4]Efficiently testing local optimality and escaping saddles for ReLU networks
  304. 6.25, 1.92, [7, 3, 8, 7] [1, 4, 1, 4]DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS
  305. 6.00, 0.82, [7, 6, 5] [3, 4, 3]Invariance and Inverse Stability under ReLU
  306. 6.00, 0.82, [5, 7, 6] [3, 4, 5]Precision Highway for Ultra Low-precision Quantization
  307. 6.00, 0.82, [7, 5, 6] [4, 3, 5]Large Scale Graph Learning From Smooth Signals
  308. 6.00, 0.82, [5, 6, 7] [3, 4, 4]L2-Nonexpansive Neural Networks
  309. 6.00, 0.82, [6, 7, 5] [4, 3, 4]Adversarial Imitation via Variational Inverse Reinforcement Learning
  310. 6.00, 1.41, [7, 4, 7] [3, 4, 3]Monge-Amp\`ere Flow for Generative Modeling
  311. 6.00, 0.00, [6, 6, 6] [3, 4, 3]INVASE: Instance-wise Variable Selection using Neural Networks
  312. 6.00, 0.82, [6, 5, 7] [4, 5, 4]DPSNet: End-to-end Deep Plane Sweep Stereo
  313. 6.00, 2.45, [6, 3, 9] [3, 4, 2]SUPERVISED POLICY UPDATE
  314. 6.00, 0.00, [6, 6, 6] [4, 5, 4]DATNet: Dual Adversarial Transfer for Low-resource Named Entity Recognition
  315. 6.00, 2.16, [8, 3, 7] [3, 4, 4]A rotation-equivariant convolutional neural network model of primary visual cortex
  316. 6.00, 1.41, [7, 7, 4] [4, 4, 4]ANYTIME MINIBATCH: EXPLOITING STRAGGLERS IN ONLINE DISTRIBUTED OPTIMIZATION
  317. 6.00, 0.71, [6, 6, 7, 5] [2, 3, 2, 4]Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection
  318. 6.00, 0.00, [6, 6, 6] [3, 3, 3]Semi-supervised Learning with Multi-Domain Sentiment Word Embeddings
  319. 6.00, 0.00, [6, 6, 6] [4, 4, 3]Variance Networks: When Expectation Does Not Meet Your Expectations
  320. 6.00, 0.82, [7, 5, 6] [4, 4, 4]Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis
  321. 6.00, 0.82, [6, 7, 5] [4, 4, 3]On Tighter Generalization Bounds for Deep Neural Networks: CNNs, ResNets, and Beyond
  322. 6.00, 1.63, [4, 6, 8] [4, 3, 5]Formal Limitations on the Measurement of Mutual Information
  323. 6.00, 0.00, [6, 6, 6] [4, 5, 3]Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers
  324. 6.00, 0.82, [7, 5, 6] [3, 5, 4]Dirichlet Variational Autoencoder
  325. 6.00, 1.41, [8, 5, 5] [4, 2, 4]Learning Kolmogorov Models for Binary Random Variables
  326. 6.00, 1.41, [4, 7, 7] [3, 5, 4]Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
  327. 6.00, 1.63, [8, 4, 6] [3, 5, 3]Are Generative Classifiers More Robust to Adversarial Attacks?
  328. 6.00, 0.82, [7, 6, 5] [3, 3, 4]EFFICIENT TWO-STEP ADVERSARIAL DEFENSE FOR DEEP NEURAL NETWORKS
  329. 6.00, 0.82, [5, 6, 7] [4, 4, 3]POLICY GENERALIZATION IN CAPACITY-LIMITED REINFORCEMENT LEARNING
  330. 6.00, 1.87, [5, 9, 4, 6] [5, 4, 5, 4]Adversarial Vulnerability of Neural Networks Increases with Input Dimension
  331. 6.00, 1.41, [7, 7, 4] [2, 3, 4]GamePad: A Learning Environment for Theorem Proving
  332. 6.00, 1.41, [7, 4, 7] [3, 5, 4]The Singular Values of Convolutional Layers
  333. 6.00, 1.41, [5, 8, 5] [4, 4, 4]code2seq: Generating Sequences from Structured Representations of Code
  334. 6.00, 1.00, [5, 7] [5, 4]PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
  335. 6.00, 1.63, [8, 4, 6] [3, 4, 4]Manifold Mixup: Learning Better Representations by Interpolating Hidden States
  336. 6.00, 0.82, [7, 5, 6] [5, 5, 3]Temporal Gaussian Mixture Layer for Videos
  337. 6.00, 0.82, [7, 5, 6] [4, 4, 5]Neural Speed Reading with Structural-Jump-LSTM
  338. 6.00, 0.82, [6, 7, 5] [4, 3, 3]Information Theoretic lower bounds on negative log likelihood
  339. 6.00, 0.71, [7, 6, 6, 5] [3, 3, 3, 4]Sinkhorn AutoEncoders
  340. 6.00, 0.00, [6, 6, 6] [4, 4, 2]Neural Networks for Modeling Source Code Edits
  341. 6.00, 0.82, [6, 5, 7] [4, 3, 4]LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator
  342. 6.00, 0.82, [7, 5, 6] [4, 5, 4]SGD Converges to Global Minimum in Deep Learning via Star-convex Path
  343. 6.00, 0.82, [5, 6, 7] [4, 4, 2]Learning from Positive and Unlabeled Data with a Selection Bias
  344. 6.00, 0.82, [5, 6, 7] [4, 3, 3]Aggregated Momentum: Stability Through Passive Damping
  345. 6.00, 1.63, [6, 8, 4] [4, 4, 4]ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.
  346. 6.00, 0.00, [6, 6, 6] [4, 4, 3]Countering Language Drift via Grounding
  347. 6.00, 0.00, [6, 6, 6] [4, 4, 4]Measuring Compositionality in Representation Learning
  348. 6.00, 2.16, [9, 5, 4] [5, 4, 4]A Biologically Inspired Visual Working Memory for Deep Networks
  349. 6.00, 0.82, [6, 5, 7] [4, 2, 3]Universal Successor Features Approximators
  350. 6.00, 1.41, [5, 8, 5] [4, 3, 5]Deep Convolutional Networks as shallow Gaussian Processes
  351. 6.00, 0.00, [6, 6, 6] [4, 3, 4]Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
  352. 6.00, 0.82, [7, 5, 6] [1, 3, 3]Variational Bayesian Phylogenetic Inference
  353. 6.00, 0.71, [5, 6, 6, 7] [4, 3, 3, 3]Relational Forward Models for Multi-Agent Learning
  354. 6.00, 0.82, [7, 5, 6] [4, 5, 3]Generative predecessor models for sample-efficient imitation learning
  355. 6.00, 0.82, [5, 6, 7] [5, 5, 3]Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile
  356. 6.00, 0.00, [6, 6, 6] [1, 2, 4]Stable Opponent Shaping in Differentiable Games
  357. 6.00, 0.82, [7, 6, 5] [4, 4, 4]DeepOBS: A Deep Learning Optimizer Benchmark Suite
  358. 6.00, 0.82, [6, 7, 5] [4, 4, 4]Policy Transfer with Strategy Optimization
  359. 6.00, 1.41, [4, 7, 7] [4, 4, 4]Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder
  360. 6.00, 0.82, [7, 6, 5] [2, 4, 3]Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems
  361. 6.00, 1.63, [8, 4, 6] [3, 4, 3]Integer Networks for Data Compression with Latent-Variable Models
  362. 6.00, 0.82, [6, 5, 7] [3, 3, 5]Residual Non-local Attention Networks for Image Restoration
  363. 6.00, 0.00, [6, 6, 6] [4, 3, 4]Information-Directed Exploration for Deep Reinforcement Learning
  364. 6.00, 0.82, [5, 7, 6] [5, 4, 4]Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs
  365. 6.00, 1.87, [7, 8, 6, 3] [4, 4, 4, 5]Gradient Descent Provably Optimizes Over-parameterized Neural Networks
  366. 6.00, 1.41, [4, 7, 7] [4, 3, 3]Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
  367. 6.00, 1.22, [4, 7, 6, 7] [5, 4, 3, 5]Dynamic Channel Pruning: Feature Boosting and Suppression
  368. 6.00, 0.82, [7, 6, 5] [3, 4, 3]Unsupervised Hyper-alignment for Multilingual Word Embeddings
  369. 6.00, 0.00, [6, 6, 6] [3, 4, 5]GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation
  370. 6.00, 0.82, [5, 7, 6] [4, 4, 3]Multi-class classification without multi-class labels
  371. 6.00, 1.63, [8, 6, 4] [3, 3, 4]On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
  372. 6.00, 0.00, [6, 6, 6] [4, 3, 4]Learning Disentangled Representations with Reference-Based Variational Autoencoders
  373. 6.00, 1.41, [7, 4, 7] [3, 5, 4]Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
  374. 6.00, 1.41, [7, 4, 7] [3, 3, 4]AutoLoss: Learning Discrete Schedule for Alternate Optimization
  375. 6.00, 0.82, [6, 7, 5] [4, 4, 4]Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures
  376. 6.00, 0.00, [6, 6, 6] [4, 4, 4]Adversarial Information Factorization
  377. 6.00, 2.16, [7, 3, 8] [3, 4, 4]ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
  378. 6.00, 0.00, [6, 6, 6] [4, 5, 4]BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop
  379. 6.00, 1.87, [6, 9, 5, 4] [3, 4, 3, 5]Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
  380. 6.00, 0.82, [5, 7, 6] [4, 4, 4]Hierarchical Reinforcement Learning with Limited Policies and Hindsight
  381. 6.00, 2.16, [5, 9, 4] [4, 4, 4]Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
  382. 6.00, 2.16, [9, 4, 5] [5, 4, 4]Detecting Memorization in ReLU Networks
  383. 6.00, 1.63, [6, 4, 8] [4, 4, 3]DADAM: A consensus-based distributed adaptive gradient method for online optimization
  384. 6.00, 1.63, [4, 6, 8] [4, 3, 4]A Systematic Study of Binary Neural Networks’ Optimisation
  385. 6.00, 1.41, [7, 4, 7] [4, 4, 5]Graph U-Net
  386. 6.00, 0.82, [7, 6, 5] [4, 3, 3]LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING
  387. 6.00, 1.41, [5, 8, 5] [3, 4, 3]On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
  388. 6.00, 1.41, [8, 5, 5] [4, 4, 4]Detecting Out-Of-Distribution Samples Using Low-Order Deep Features Statistics
  389. 6.00, 0.82, [5, 7, 6] [4, 4, 4]Decoupled Weight Decay Regularization
  390. 6.00, 0.00, [6, 6, 6] [5, 4, 5]Diversity and Depth in Per-Example Routing Models
  391. 6.00, 1.41, [5, 5, 8] [4, 4, 4]ProxQuant: Quantized Neural Networks via Proximal Operators
  392. 6.00, 0.00, [6, 6, 6] [4, 3, 4]Wasserstein Barycenter Model Ensembling
  393. 6.00, 0.00, [6, 6, 6] [3, 4, 3]Stochastic Gradient Push for Distributed Deep Learning
  394. 6.00, 0.82, [5, 7, 6] [3, 1, 3]DOM-Q-NET: Grounded RL on Structured Language
  395. 6.00, 1.41, [8, 5, 5] [5, 3, 5]Meta-Learning with Latent Embedding Optimization
  396. 6.00, 0.00, [6, 6, 6] [3, 3, 4]Reinforcement Learning with Perturbed Rewards
  397. 6.00, 0.82, [5, 6, 7] [3, 3, 3]MEAN-FIELD ANALYSIS OF BATCH NORMALIZATION
  398. 6.00, 1.63, [8, 4, 6] [3, 4, 3]Learning what and where to attend with humans in the loop
  399. 6.00, 0.82, [7, 6, 5] [4, 5, 3]How to train your MAML
  400. 6.00, 0.82, [7, 6, 5] [4, 4, 3]Learning Heuristics for Automated Reasoning through Reinforcement Learning
  401. 6.00, 1.63, [4, 8, 6] [4, 3, 2]Lyapunov-based Safe Policy Optimization
  402. 6.00, 0.00, [6, 6, 6] [4, 3, 3]Dimension-Free Bounds for Low-Precision Training
  403. 6.00, 1.41, [4, 7, 7] [3, 4, 4]Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision
  404. 6.00, 0.00, [6, 6, 6] [4, 4, 4]Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images
  405. 6.00, 1.63, [4, 8, 6] [3, 4, 3]Unsupervised Adversarial Image Reconstruction
  406. 6.00, 0.00, [6, 6, 6] [4, 2, 3]Environment Probing Interaction Policies
  407. 6.00, 0.82, [5, 7, 6] [5, 2, 3]Neural Logic Machines
  408. 6.00, 0.00, [6, 6, 6] [3, 5, 5]Graph Transformer
  409. 6.00, 1.41, [5, 8, 5] [3, 2, 5]Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors
  410. 6.00, 0.82, [5, 6, 7] [3, 3, 4]Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
  411. 6.00, 0.00, [6, 6, 6] [2, 4, 3]Improving the Generalization of Adversarial Training with Domain Adaptation
  412. 6.00, 1.63, [8, 6, 4] [2, 4, 4]Learning Abstract Models for Long-Horizon Exploration
  413. 6.00, 0.82, [6, 7, 5] [3, 3, 4]A Direct Approach to Robust Deep Learning Using Adversarial Networks
  414. 6.00, 0.82, [5, 7, 6] [4, 4, 3]Spreading vectors for similarity search
  415. 6.00, 1.63, [4, 6, 8] [4, 4, 4]Probabilistic Planning with Sequential Monte Carlo
  416. 6.00, 0.82, [6, 7, 5] [3, 3, 2]Recall Traces: Backtracking Models for Efficient Reinforcement Learning
  417. 6.00, 0.82, [6, 5, 7] [3, 3, 3]Value Propagation Networks
  418. 6.00, 0.00, [6, 6, 6] [4, 5, 2]A Closer Look at Few-shot Classification
  419. 6.00, 1.63, [4, 8, 6] [5, 3, 3]Learning to Learn with Conditional Class Dependencies
  420. 6.00, 0.00, [6, 6, 6] [5, 4, 5]TarMAC: Targeted Multi-Agent Communication
  421. 6.00, 0.82, [5, 6, 7] [5, 4, 3]A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax
  422. 6.00, 0.00, [6, 6, 6] [3, 3, 3]A MAX-AFFINE SPLINE PERSPECTIVE OF RECURRENT NEURAL NETWORKS
  423. 6.00, 0.00, [6, 6, 6] [3, 3, 3]Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
  424. 6.00, 0.82, [7, 5, 6] [4, 4, 4]Diverse Machine Translation with a Single Multinomial Latent Variable
  425. 6.00, 0.00, [6, 6, 6] [2, 4, 4]Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
  426. 6.00, 0.00, [6, 6, 6] [3, 3, 3]Characterizing Audio Adversarial Examples Using Temporal Dependency
  427. 6.00, 1.63, [8, 6, 4] [5, 4, 5]Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling
  428. 6.00, 0.82, [6, 7, 5] [2, 2, 5]The Variational Deficiency Bottleneck
  429. 6.00, 0.82, [7, 6, 5] [4, 4, 4]Combinatorial Attacks on Binarized Neural Networks
  430. 6.00, 0.82, [5, 7, 6] [4, 2, 3]Contingency-Aware Exploration in Reinforcement Learning
  431. 6.00, 0.82, [7, 5, 6] [5, 4, 5]Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
  432. 6.00, 0.00, [6, 6, 6] [2, 2, 4]Proxy-less Architecture Search via Binarized Path Learning
  433. 6.00, 1.41, [4, 7, 7] [4, 4, 4]Revealing interpretable object representations from human behavior
  434. 6.00, 0.00, [6, 6, 6] [4, 4, 5]Multi-step Reasoning for Open-domain Question Answering
  435. 6.00, 0.00, [6, 6, 6] [3, 3, 4]Single Shot Neural Architecture Search Via Direct Sparse Optimization
  436. 5.75, 0.83, [7, 5, 6, 5] [3, 3, 3, 4]On the Spectral Bias of Neural Networks
  437. 5.75, 0.83, [5, 7, 6, 5] [3, 4, 3, 3]Modeling Parts, Structure, and System Dynamics via Predictive Learning
  438. 5.75, 0.83, [5, 6, 5, 7] [4, 4, 5, 3]An Alarm System for Segmentation Algorithm Based on Shape Model
  439. 5.75, 0.43, [6, 5, 6, 6] [4, 4, 3, 4]Two-Timescale Networks for Nonlinear Value Function Approximation
  440. 5.67, 0.94, [7, 5, 5] [5, 5, 4](Unconstrained) Beam Search is Sensitive to Large Search Discrepancies
  441. 5.67, 1.25, [7, 6, 4] [4, 1, 4]CONTROLLING COVARIATE SHIFT USING EQUILIBRIUM NORMALIZATION OF WEIGHTS
  442. 5.67, 0.47, [5, 6, 6] [4, 3, 3]Amortized Context Vector Inference for Sequence-to-Sequence Networks
  443. 5.67, 0.94, [5, 5, 7] [4, 5, 4]The meaning of “most” for visual question answering models
  444. 5.67, 2.05, [8, 3, 6] [4, 2, 3]Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm
  445. 5.67, 0.94, [5, 7, 5] [4, 4, 3]A unified theory of adaptive stochastic gradient descent as Bayesian filtering
  446. 5.67, 0.47, [5, 6, 6] [4, 4, 4]Laplacian Smoothing Gradient Descent
  447. 5.67, 1.25, [4, 7, 6] [4, 4, 4]Explicit Information Placement on Latent Variables using Auxiliary Generative Modelling Task
  448. 5.67, 1.70, [4, 5, 8] [4, 4, 4]Discriminative Active Learning
  449. 5.67, 1.25, [4, 7, 6] [4, 4, 3]A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
  450. 5.67, 1.25, [4, 6, 7] [4, 4, 3]Generating Liquid Simulations with Deformation-aware Neural Networks
  451. 5.67, 0.94, [5, 7, 5] [4, 2, 5]A Kernel Random Matrix-Based Approach for Sparse PCA
  452. 5.67, 0.47, [6, 5, 6] [4, 4, 4]Identifying Generalization Properties in Neural Networks
  453. 5.67, 0.94, [5, 5, 7] [4, 4, 3]Hierarchical interpretations for neural network predictions
  454. 5.67, 0.47, [6, 5, 6] [4, 1, 3]Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps
  455. 5.67, 0.47, [6, 5, 6] [1, 3, 4]M^3RL: Mind-aware Multi-agent Management Reinforcement Learning
  456. 5.67, 0.47, [6, 6, 5] [4, 4, 2]Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening
  457. 5.67, 0.47, [6, 5, 6] [3, 3, 3]Remember and Forget for Experience Replay
  458. 5.67, 0.94, [5, 5, 7] [4, 4, 4]Fast adversarial training for semi-supervised learning
  459. 5.67, 0.47, [6, 6, 5] [4, 3, 3]An Information-Theoretic Metric of Transferability for Task Transfer Learning
  460. 5.67, 1.25, [6, 4, 7] [4, 4, 4]Convolutional CRFs for Semantic Segmentation
  461. 5.67, 0.47, [6, 6, 5] [3, 5, 3]Dynamic Early Terminating of Multiply Accumulate Operations for Saving Computation Cost in Convolutional Neural Networks
  462. 5.67, 1.25, [4, 6, 7] [4, 4, 2]Causal importance of orientation selectivity for generalization in image recognition
  463. 5.67, 0.94, [5, 7, 5] [3, 3, 4]Function Space Particle Optimization for Bayesian Neural Networks
  464. 5.67, 0.47, [6, 5, 6] [4, 4, 4]Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
  465. 5.67, 1.25, [4, 7, 6] [4, 5, 4]Visual Reasoning by Progressive Module Networks
  466. 5.67, 0.47, [6, 6, 5] [3, 4, 3]Incremental training of multi-generative adversarial networks
  467. 5.67, 0.47, [6, 5, 6] [4, 3, 4]Projective Subspace Networks For Few Shot Learning
  468. 5.67, 0.94, [5, 7, 5] [4, 4, 3]DANA: Scalable Out-of-the-box Distributed ASGD Without Retuning
  469. 5.67, 1.25, [6, 7, 4] [4, 5, 4]A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation
  470. 5.67, 1.25, [4, 7, 6] [4, 3, 4]Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
  471. 5.67, 0.94, [5, 7, 5] [4, 4, 4]Cramer-Wold AutoEncoder
  472. 5.67, 0.47, [6, 6, 5] [5, 3, 4]Better Generalization with On-the-fly Dataset Denoising
  473. 5.67, 1.25, [4, 7, 6] [3, 4, 4]Talk The Walk: Navigating Grids in New York City through Grounded Dialogue
  474. 5.67, 0.47, [5, 6, 6] [4, 4, 4]Efficient Lifelong Learning with A-GEM
  475. 5.67, 0.94, [5, 5, 7] [3, 3, 5]Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models
  476. 5.67, 0.94, [5, 5, 7] [4, 4, 3]Learning Implicit Generative Models by Teaching Explicit Ones
  477. 5.67, 1.25, [4, 7, 6] [5, 4, 3]PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning
  478. 5.67, 2.36, [4, 9, 4] [2, 3, 4]PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation
  479. 5.67, 0.47, [5, 6, 6] [5, 5, 4]State-Regularized Recurrent Networks
  480. 5.67, 2.36, [9, 4, 4] [4, 4, 3]The Problem of Model Completion
  481. 5.67, 0.47, [6, 5, 6] [4, 5, 4]Zero-Resource Multilingual Model Transfer: Learning What to Share
  482. 5.67, 0.94, [7, 5, 5] [4, 3, 3]Learning to Make Analogies by Contrasting Abstract Relational Structure
  483. 5.67, 0.47, [6, 6, 5] [2, 5, 3]Towards Understanding Regularization in Batch Normalization
  484. 5.67, 1.25, [6, 7, 4] [4, 4, 4]ACCELERATING NONCONVEX LEARNING VIA REPLICA EXCHANGE LANGEVIN DIFFUSION
  485. 5.67, 0.47, [6, 6, 5] [2, 5, 4]Identifying Bias in AI using Simulation
  486. 5.67, 0.47, [6, 5, 6] [3, 4, 4]Understanding GANs via Generalization Analysis for Disconnected Support
  487. 5.67, 0.47, [6, 5, 6] [4, 3, 3]Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior
  488. 5.67, 1.25, [7, 4, 6] [3, 3, 4]Guiding Physical Intuition with Neural Stethoscopes
  489. 5.67, 0.94, [5, 7, 5] [4, 4, 5]Whitening and Coloring transform for GANs
  490. 5.67, 0.47, [5, 6, 6] [3, 4, 5]Efficient Codebook and Factorization for Second Order Representation Learning
  491. 5.67, 0.47, [6, 6, 5] [4, 3, 5]Adversarial Attacks on Node Embeddings
  492. 5.67, 0.47, [6, 6, 5] [4, 4, 4]Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
  493. 5.67, 0.47, [5, 6, 6] [3, 2, 3]Learning Neural Random Fields with Inclusive Auxiliary Generators
  494. 5.67, 0.47, [6, 6, 5] [4, 3, 3]Analysing Mathematical Reasoning Abilities of Neural Models
  495. 5.67, 0.47, [6, 5, 6] [4, 3, 4]Learning Representations of Sets through Optimized Permutations
  496. 5.67, 0.47, [6, 5, 6] [3, 4, 4]CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
  497. 5.67, 0.94, [5, 7, 5] [5, 4, 3]Backprop with Approximate Activations for Memory-efficient Network Training
  498. 5.67, 0.94, [5, 5, 7] [3, 4, 4]Learning models for visual 3D localization with implicit mapping
  499. 5.67, 1.25, [4, 7, 6] [5, 4, 4]Estimating Information Flow in DNNs
  500. 5.67, 0.94, [5, 5, 7] [3, 3, 3]Adversarial Exploration Strategy for Self-Supervised Imitation Learning
  501. 5.67, 0.94, [7, 5, 5] [4, 5, 5]signSGD with Majority Vote is Communication Efficient and Byzantine Fault Tolerant
  502. 5.67, 0.94, [7, 5, 5] [3, 3, 3]Predicted Variables in Programming
  503. 5.67, 0.47, [5, 6, 6] [5, 4, 3]Stochastic Adversarial Video Prediction
  504. 5.67, 1.70, [4, 5, 8] [4, 4, 3]Cross-Entropy Loss Leads To Poor Margins
  505. 5.67, 0.47, [6, 6, 5] [4, 4, 1]Kernel Recurrent Learning (KeRL)
  506. 5.67, 1.25, [6, 4, 7] [5, 4, 4]Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
  507. 5.67, 0.47, [6, 5, 6] [4, 5, 2]Overcoming Multi-model Forgetting
  508. 5.67, 0.94, [7, 5, 5] [4, 4, 4]ADAPTIVE NETWORK SPARSIFICATION VIA DEPENDENT VARIATIONAL BETA-BERNOULLI DROPOUT
  509. 5.67, 0.94, [5, 5, 7] [4, 5, 5]Domain Adaptation for Structured Output via Disentangled Patch Representations
  510. 5.67, 0.47, [6, 6, 5] [5, 2, 4]Large-Scale Answerer in Questioner’s Mind for Visual Dialog Question Generation
  511. 5.67, 1.25, [6, 4, 7] [4, 4, 3]Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  512. 5.67, 1.25, [6, 4, 7] [2, 4, 4]Excessive Invariance Causes Adversarial Vulnerability
  513. 5.67, 0.47, [6, 6, 5] [4, 3, 4]Adversarial Audio Synthesis
  514. 5.67, 0.94, [5, 7, 5] [3, 3, 3]Spectral Inference Networks: Unifying Deep and Spectral Learning
  515. 5.67, 2.49, [9, 3, 5] [4, 4, 4]Unsupervised Neural Multi-Document Abstractive Summarization of Reviews
  516. 5.67, 1.25, [6, 4, 7] [4, 5, 4]Learning Multimodal Graph-to-Graph Translation for Molecule Optimization
  517. 5.67, 0.47, [6, 5, 6] [3, 4, 4]Discovery of natural language concepts in individual units
  518. 5.67, 0.94, [5, 5, 7] [4, 4, 4]Unsupervised Learning of Sentence Representations Using Sequence Consistency
  519. 5.67, 0.94, [5, 7, 5] [4, 4, 3]Improving Sequence-to-Sequence Learning via Optimal Transport
  520. 5.67, 1.25, [6, 4, 7] [5, 4, 3]MILE: A Multi-Level Framework for Scalable Graph Embedding
  521. 5.67, 1.25, [6, 4, 7] [4, 3, 3]Learning to Represent Edits
  522. 5.67, 0.47, [6, 6, 5] [4, 3, 3]Out-of-Sample Extrapolation with Neuron Editing
  523. 5.67, 0.94, [5, 5, 7] [4, 5, 4]Improving Sentence Representations with Multi-view Frameworks
  524. 5.67, 0.47, [6, 5, 6] [4, 3, 5]Generalizable Adversarial Training via Spectral Normalization
  525. 5.67, 1.89, [3, 7, 7] [4, 4, 3]Learning Entropic Wasserstein Embeddings
  526. 5.67, 0.47, [5, 6, 6] [2, 1, 3]Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
  527. 5.67, 0.47, [5, 6, 6] [5, 4, 4]Seq2Slate: Re-ranking and Slate Optimization with RNNs
  528. 5.67, 0.47, [5, 6, 6] [4, 4, 3]Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
  529. 5.67, 0.94, [7, 5, 5] [5, 3, 3]A new dog learns old tricks: RL finds classic optimization algorithms
  530. 5.67, 1.25, [4, 6, 7] [3, 3, 3]Variational Autoencoder with Arbitrary Conditioning
  531. 5.67, 0.47, [5, 6, 6] [5, 4, 4]Neural Program Repair by Jointly Learning to Localize and Repair
  532. 5.67, 0.94, [7, 5, 5] [4, 4, 4]Shallow Learning For Deep Networks
  533. 5.67, 1.25, [4, 7, 6] [4, 4, 2]Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition
  534. 5.67, 0.47, [6, 5, 6] [5, 4, 5]Deep Probabilistic Video Compression
  535. 5.67, 0.47, [6, 6, 5] [3, 4, 5]A More Globally Accurate Dimensionality Reduction Method Using Triplets
  536. 5.67, 1.25, [6, 4, 7] [4, 5, 4]Adaptive Gradient Methods with Dynamic Bound of Learning Rate
  537. 5.67, 0.47, [6, 5, 6] [2, 4, 1]Adversarially Learned Mixture Model
  538. 5.67, 1.25, [4, 7, 6] [4, 2, 2]Clean-Label Backdoor Attacks
  539. 5.67, 1.25, [7, 4, 6] [2, 4, 4]Perception-Aware Point-Based Value Iteration for Partially Observable Markov Decision Processes
  540. 5.67, 0.47, [5, 6, 6] [4, 3, 4]Trace-back along capsules and its application on semantic segmentation
  541. 5.67, 1.25, [7, 4, 6] [4, 4, 5]Hallucinations in Neural Machine Translation
  542. 5.67, 0.47, [5, 6, 6] [3, 1, 5]Learning Programmatically Structured Representations with Perceptor Gradients
  543. 5.67, 1.89, [7, 7, 3] [4, 5, 5]Learning Exploration Policies for Navigation
  544. 5.67, 0.94, [7, 5, 5] [3, 3, 4]Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
  545. 5.67, 0.94, [5, 5, 7] [4, 3, 4]Open-Ended Content-Style Recombination Via Leakage Filtering
  546. 5.67, 2.36, [9, 4, 4] [4, 4, 4]Bayesian Modelling and Monte Carlo Inference for GAN
  547. 5.67, 0.47, [6, 5, 6] [4, 3, 3]Multi-objective training of Generative Adversarial Networks with multiple discriminators
  548. 5.67, 1.25, [4, 7, 6] [4, 3, 3]Knowledge Representation for Reinforcement Learning using General Value Functions
  549. 5.67, 0.47, [6, 6, 5] [5, 5, 3]Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
  550. 5.67, 1.25, [7, 6, 4] [4, 4, 4]CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
  551. 5.67, 1.25, [7, 4, 6] [4, 3, 5]NECST: Neural Joint Source-Channel Coding
  552. 5.67, 0.94, [7, 5, 5] [4, 3, 4]Nested Dithered Quantization for Communication Reduction in Distributed Training
  553. 5.67, 0.94, [5, 7, 5] [5, 3, 4]Explaining Image Classifiers by Counterfactual Generation
  554. 5.67, 1.25, [6, 7, 4] [5, 4, 4]The Expressive Power of Deep Neural Networks with Circulant Matrices
  555. 5.67, 0.47, [6, 5, 6] [3, 4, 4]Learning what you can do before doing anything
  556. 5.67, 0.47, [6, 6, 5] [4, 4, 4]Language Model Pre-training for Hierarchical Document Representations
  557. 5.67, 1.25, [6, 7, 4] [3, 4, 4]Efficient Augmentation via Data Subsampling
  558. 5.67, 0.47, [5, 6, 6] [4, 3, 4]Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
  559. 5.67, 0.94, [7, 5, 5] [4, 4, 4]Hierarchically-Structured Variational Autoencoders for Long Text Generation
  560. 5.67, 0.94, [5, 5, 7] [3, 4, 4]Where Off-Policy Deep Reinforcement Learning Fails
  561. 5.67, 1.25, [4, 7, 6] [4, 4, 4]TENSOR RING NETS ADAPTED DEEP MULTI-TASK LEARNING
  562. 5.67, 0.47, [6, 5, 6] [3, 5, 4]A Variational Dirichlet Framework for Out-of-Distribution Detection
  563. 5.67, 0.94, [5, 7, 5] [4, 3, 4]Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression
  564. 5.67, 1.70, [5, 8, 4] [4, 3, 4]Augment your batch: better training with larger batches
  565. 5.67, 0.94, [5, 7, 5] [4, 2, 5]On Difficulties of Probability Distillation
  566. 5.67, 0.47, [5, 6, 6] [2, 4, 3]Top-Down Neural Model For Formulae
  567. 5.67, 0.94, [5, 5, 7] [4, 4, 4]Manifold regularization with GANs for semi-supervised learning
  568. 5.67, 0.94, [5, 5, 7] [5, 3, 4]Cross-Task Knowledge Transfer for Visually-Grounded Navigation
  569. 5.67, 1.25, [6, 7, 4] [3, 2, 4]Rotation Equivariant Networks via Conic Convolution and the DFT
  570. 5.67, 1.89, [3, 7, 7] [5, 4, 5]Small steps and giant leaps: Minimal Newton solvers for Deep Learning
  571. 5.67, 1.25, [7, 4, 6] [4, 3, 4]Beyond Greedy Ranking: Slate Optimization via List-CVAE
  572. 5.67, 0.47, [5, 6, 6] [3, 4, 3]Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution
  573. 5.67, 0.47, [5, 6, 6] [4, 4, 4]Learning to Augment Influential Data
  574. 5.67, 1.25, [7, 4, 6] [3, 3, 3]Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference
  575. 5.67, 1.70, [8, 5, 4] [3, 3, 4]Cost-Sensitive Robustness against Adversarial Examples
  576. 5.67, 0.47, [6, 6, 5] [4, 1, 4]Learning to Design RNA
  577. 5.67, 1.25, [7, 4, 6] [3, 4, 3]Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure
  578. 5.67, 0.94, [5, 5, 7] [3, 3, 3]Finite Automata Can be Linearly Decoded from Language-Recognizing RNNs
  579. 5.67, 0.47, [5, 6, 6] [5, 4, 4]Selfless Sequential Learning
  580. 5.67, 0.47, [6, 6, 5] [4, 4, 4]Modeling the Long Term Future in Model-Based Reinforcement Learning
  581. 5.67, 1.25, [7, 4, 6] [3, 4, 4]Poincare Glove: Hyperbolic Word Embeddings
  582. 5.67, 0.47, [6, 5, 6] [5, 5, 4]Rethinking the Value of Network Pruning
  583. 5.67, 0.94, [5, 5, 7] [2, 4, 4]DL2: Training and Querying Neural Networks with Logic
  584. 5.50, 1.12, [7, 5, 4, 6] [4, 4, 4, 4]Computing committor functions for the study of rare events using deep learning with importance sampling
  585. 5.50, 0.50, [5, 6, 6, 5] [4, 4, 3, 4]Interactive Agent Modeling by Learning to Probe
  586. 5.50, 0.87, [6, 6, 6, 4] [2, 2, 3, 4]Multi-way Encoding for Robustness to Adversarial Attacks
  587. 5.50, 0.87, [7, 5, 5, 5] [3, 3, 4, 4]On the Margin Theory of Feedforward Neural Networks
  588. 5.50, 0.87, [6, 6, 6, 4] [2, 2, 4, 5]CAML: Fast Context Adaptation via Meta-Learning
  589. 5.50, 0.50, [5, 6] [3, 2]Policy Optimization via Stochastic Recursive Gradient Algorithm
  590. 5.33, 0.47, [6, 5, 5] [3, 4, 3]The Universal Approximation Power of Finite-Width Deep ReLU Networks
  591. 5.33, 0.47, [5, 6, 5] [3, 3, 5]Classification from Positive, Unlabeled and Biased Negative Data
  592. 5.33, 1.25, [4, 7, 5] [3, 4, 3]Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation
  593. 5.33, 0.47, [5, 6, 5] [3, 4, 4]Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
  594. 5.33, 0.94, [6, 6, 4] [4, 2, 3]Lipschitz regularized Deep Neural Networks converge and generalize
  595. 5.33, 0.47, [5, 5, 6] [3, 4, 1]Playing the Game of Universal Adversarial Perturbations
  596. 5.33, 0.94, [4, 6, 6] [4, 3, 3]Provable Guarantees on Learning Hierarchical Generative Models with Deep CNNs
  597. 5.33, 2.49, [6, 8, 2] [4, 4, 4]Caveats for information bottleneck in deterministic scenarios
  598. 5.33, 1.25, [5, 4, 7] [4, 4, 4]Clinical Risk: wavelet reconstruction networks for marked point processes
  599. 5.33, 1.70, [7, 6, 3] [3, 4, 2] The relativistic discriminator: a key element missing from standard GAN
  600. 5.33, 0.47, [5, 6, 5] [4, 3, 5]On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
  601. 5.33, 0.47, [5, 5, 6] [4, 2, 4]Adaptive Pruning of Neural Language Models for Mobile Devices
  602. 5.33, 0.94, [6, 4, 6] [3, 4, 4]Reducing Overconfident Errors outside the Known Distribution
  603. 5.33, 0.94, [6, 4, 6] [4, 5, 4]Learning to Understand Goal Specifications by Modelling Reward
  604. 5.33, 0.94, [6, 4, 6] [3, 4, 5]LEARNING FACTORIZED REPRESENTATIONS FOR OPEN-SET DOMAIN ADAPTATION
  605. 5.33, 0.47, [5, 5, 6] [4, 4, 4]SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
  606. 5.33, 0.47, [5, 5, 6] [2, 4, 3]An experimental study of layer-level training speed and its impact on generalization
  607. 5.33, 0.47, [6, 5, 5] [4, 4, 3]Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
  608. 5.33, 1.89, [4, 4, 8] [4, 4, 3]DecayNet: A Study on the Cell States of Long Short Term Memories
  609. 5.33, 0.47, [5, 5, 6] [3, 4, 3]Training generative latent models by variational f-divergence minimization
  610. 5.33, 1.25, [5, 7, 4] [4, 5, 5]Domain Generalization via Invariant Representation under Domain-Class Dependency
  611. 5.33, 1.25, [5, 7, 4] [4, 3, 4]Distribution-Interpolation Trade off in Generative Models
  612. 5.33, 0.47, [5, 6, 5] [5, 2, 3]Purchase as Reward : Session-based Recommendation by Imagination Reconstruction
  613. 5.33, 0.47, [6, 5, 5] [3, 3, 4]Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective
  614. 5.33, 0.94, [6, 6, 4] [3, 3, 2]Exploring and Enhancing the Transferability of Adversarial Examples
  615. 5.33, 1.25, [7, 4, 5] [5, 4, 3]Switching Linear Dynamics for Variational Bayes Filtering
  616. 5.33, 1.25, [5, 7, 4] [4, 3, 4]The loss landscape of overparameterized neural networks
  617. 5.33, 0.94, [6, 4, 6] [4, 4, 3]Curiosity-Driven Experience Prioritization via Density Estimation
  618. 5.33, 0.47, [5, 5, 6] [5, 5, 3]Generalization and Regularization in DQN
  619. 5.33, 1.25, [4, 5, 7] [3, 5, 2]Invariant-equivariant representation learning for multi-class data
  620. 5.33, 2.62, [9, 3, 4] [4, 5, 4] Large-Scale Visual Speech Recognition
  621. 5.33, 0.47, [5, 5, 6] [4, 4, 4]RoC-GAN: Robust Conditional GAN
  622. 5.33, 1.25, [7, 5, 4] [2, 2, 2]On the Turing Completeness of Modern Neural Network Architectures
  623. 5.33, 0.47, [5, 6, 5] [4, 4, 2]The Unusual Effectiveness of Averaging in GAN Training
  624. 5.33, 0.94, [6, 6, 4] [4, 5, 4]Graph Wavelet Neural Network
  625. 5.33, 0.47, [6, 5, 5] [4, 4, 5]Gaussian-gated LSTM: Improved convergence by reducing state updates
  626. 5.33, 0.94, [6, 4, 6] [3, 3, 3]Meta Domain Adaptation: Meta-Learning for Few-Shot Learning under Domain Shift
  627. 5.33, 0.47, [5, 5, 6] [4, 4, 5]Learning to encode spatial relations from natural language
  628. 5.33, 0.47, [6, 5, 5] [3, 3, 3]Skip-gram word embeddings in hyperbolic space
  629. 5.33, 0.47, [6, 5, 5] [4, 4, 4]Graph Matching Networks for Learning the Similarity of Graph Structured Objects
  630. 5.33, 1.25, [5, 7, 4] [4, 4, 3]Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation
  631. 5.33, 1.70, [7, 3, 6] [4, 5, 4]Learning From the Experience of Others: Approximate Empirical Bayes in Neural Networks
  632. 5.33, 2.05, [8, 3, 5] [4, 5, 4]DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns
  633. 5.33, 0.47, [6, 5, 5] [4, 3, 3]Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning
  634. 5.33, 0.94, [6, 6, 4] [4, 3, 4]Learning to Generate Parameters from Natural Languages for Graph Neural Networks
  635. 5.33, 1.25, [7, 5, 4] [5, 3, 4]Adaptive Neural Trees
  636. 5.33, 0.47, [6, 5, 5] [2, 3, 3]Learning Internal Dense But External Sparse Structures of Deep Neural Network
  637. 5.33, 1.25, [5, 7, 4] [4, 3, 2]DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING
  638. 5.33, 1.25, [4, 5, 7] [5, 4, 4]Unseen Action Recognition with Multimodal Learning
  639. 5.33, 0.47, [5, 5, 6] [3, 4, 3]Equi-normalization of Neural Networks
  640. 5.33, 0.47, [5, 5, 6] [5, 4, 2]Adversarial Sampling for Active Learning
  641. 5.33, 1.70, [6, 7, 3] [5, 4, 3]CEM-RL: Combining evolutionary and gradient-based methods for policy search
  642. 5.33, 1.25, [7, 5, 4] [4, 3, 4]Overcoming Catastrophic Forgetting via Model Adaptation
  643. 5.33, 0.47, [6, 5, 5] [3, 4, 4]Hierarchically Clustered Representation Learning
  644. 5.33, 1.89, [8, 4, 4] [4, 4, 5]Neural Causal Discovery with Learnable Input Noise
  645. 5.33, 0.47, [5, 6, 5] [5, 3, 4]h-detach: Modifying the LSTM Gradient Towards Better Optimization
  646. 5.33, 0.94, [4, 6, 6] [3, 4, 4]Structured Neural Summarization
  647. 5.33, 0.94, [4, 6, 6] [3, 4, 4]Soft Q-Learning with Mutual-Information Regularization
  648. 5.33, 0.47, [5, 6, 5] [5, 3, 4]Set Transformer
  649. 5.33, 0.47, [5, 5, 6] [3, 4, 4]Learning data-derived privacy preserving representations from information metrics
  650. 5.33, 0.47, [6, 5, 5] [4, 4, 2]EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
  651. 5.33, 0.47, [5, 6, 5] [3, 2, 3]Negotiating Team Formation Using Deep Reinforcement Learning
  652. 5.33, 1.25, [4, 7, 5] [4, 3, 3]Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
  653. 5.33, 0.47, [5, 5, 6] [4, 4, 4]Lorentzian Distance Learning
  654. 5.33, 1.70, [6, 7, 3] [2, 4, 2]Cohen Welling bases & SO(2)-Equivariant classifiers using Tensor nonlinearity.
  655. 5.33, 0.47, [5, 5, 6] [4, 5, 4]EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models
  656. 5.33, 0.47, [5, 6, 5] [5, 4, 4]Exploring Curvature Noise in Large-Batch Stochastic Optimization
  657. 5.33, 0.94, [4, 6, 6] [4, 4, 4]Transformer-XL: Language Modeling with Longer-Term Dependency
  658. 5.33, 0.47, [5, 5, 6] [3, 3, 3]The Case for Full-Matrix Adaptive Regularization
  659. 5.33, 0.94, [6, 6, 4] [4, 5, 5]BLISS in Non-Isometric Embedding Spaces
  660. 5.33, 1.70, [6, 3, 7] [4, 2, 4]Learning-Based Frequency Estimation Algorithms
  661. 5.33, 0.94, [4, 6, 6] [4, 3, 4]Hint-based Training for Non-Autoregressive Translation
  662. 5.33, 2.62, [9, 4, 3] [4, 4, 5]An adaptive homeostatic algorithm for the unsupervised learning of visual features
  663. 5.33, 1.89, [4, 8, 4] [3, 4, 4]A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks
  664. 5.33, 0.94, [6, 6, 4] [3, 4, 4]Knowledge Distillation from Few Samples
  665. 5.33, 0.47, [6, 5, 5] [4, 4, 2]Measuring and regularizing networks in function space
  666. 5.33, 0.47, [5, 6, 5] [4, 3, 4]Graph Classification with Geometric Scattering
  667. 5.33, 0.47, [5, 5, 6] [3, 3, 2]Selective Convolutional Units: Improving CNNs via Channel Selectivity
  668. 5.33, 0.47, [5, 5, 6] [4, 3, 5]Learning to Decompose Compound Questions with Reinforcement Learning
  669. 5.33, 0.47, [5, 6, 5] [4, 2, 4]Infinitely Deep Infinite-Width Networks
  670. 5.33, 0.47, [5, 5, 6] [4, 3, 4]State-Denoised Recurrent Neural Networks
  671. 5.33, 0.47, [5, 6, 5] [4, 4, 4]Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
  672. 5.33, 0.47, [5, 6, 5] [4, 5, 4]CDeepEx: Contrastive Deep Explanations
  673. 5.33, 1.25, [4, 5, 7] [3, 3, 5]Verification of Non-Linear Specifications for Neural Networks
  674. 5.33, 1.25, [7, 5, 4] [4, 4, 5]LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS
  675. 5.33, 0.47, [5, 6, 5] [4, 3, 3]Learning State Representations in Complex Systems with Multimodal Data
  676. 5.33, 1.70, [3, 7, 6] [3, 3, 3]Transfer and Exploration via the Information Bottleneck
  677. 5.33, 0.47, [6, 5, 5] [4, 3, 3]Unsupervised Conditional Generation using noise engineered mode matching GAN
  678. 5.33, 0.94, [6, 4, 6] [4, 3, 3]Learning to Describe Scenes with Programs
  679. 5.33, 2.05, [8, 5, 3] [4, 3, 4]Human-level Protein Localization with Convolutional Neural Networks
  680. 5.33, 1.70, [3, 7, 6] [5, 5, 3]Improved Language Modeling by Decoding the Past
  681. 5.33, 0.47, [5, 5, 6] [3, 3, 4]Amortized Bayesian Meta-Learning
  682. 5.33, 1.25, [7, 4, 5] [4, 5, 4]Coverage and Quality Driven Training of Generative Image Models
  683. 5.33, 0.47, [5, 5, 6] [3, 5, 3]Learning space time dynamics with PDE guided neural networks
  684. 5.33, 0.94, [6, 4, 6] [3, 3, 3]NLProlog: Reasoning with Weak Unification for Natural Language Question Answering
  685. 5.33, 0.94, [4, 6, 6] [4, 3, 3]Actor-Attention-Critic for Multi-Agent Reinforcement Learning
  686. 5.33, 1.25, [4, 5, 7] [4, 3, 4]Deep learning generalizes because the parameter-function map is biased towards simple functions
  687. 5.33, 1.25, [7, 4, 5] [4, 3, 4]Learning protein sequence embeddings using information from structure
  688. 5.33, 0.47, [5, 6, 5] [4, 3, 3]Meta Learning with Fast/Slow Learners
  689. 5.33, 1.70, [3, 6, 7] [4, 4, 4]Meta-Learning for Contextual Bandit Exploration
  690. 5.33, 1.25, [4, 7, 5] [3, 4, 5]Understanding & Generalizing AlphaGo Zero
  691. 5.33, 1.25, [4, 7, 5] [3, 4, 4]Random mesh projectors for inverse problems
  692. 5.33, 1.89, [8, 4, 4] [4, 4, 5]Deep Anomaly Detection with Outlier Exposure
  693. 5.33, 0.47, [5, 6, 5] [4, 4, 4]Probabilistic Model-Based Dynamic Architecture Search
  694. 5.33, 0.47, [6, 5, 5] [4, 2, 3]Mimicking actions is a good strategy for beginners: Fast Reinforcement Learning with Expert Action Sequences
  695. 5.33, 1.25, [5, 7, 4] [5, 5, 5]Universal Successor Features for Transfer Reinforcement Learning
  696. 5.33, 1.25, [7, 5, 4] [4, 4, 4]Combining Neural Networks with Personalized PageRank for Classification on Graphs
  697. 5.33, 1.25, [5, 4, 7] [4, 5, 4]AIM: Adversarial Inference by Matching Priors and Conditionals
  698. 5.33, 1.25, [4, 7, 5] [3, 4, 4]DON’T JUDGE A BOOK BY ITS COVER – ON THE DYNAMICS OF RECURRENT NEURAL NETWORKS
  699. 5.33, 1.25, [4, 7, 5] [3, 4, 4]The Nonlinearity Coefficient – Predicting Generalization in Deep Neural Networks
  700. 5.33, 1.25, [7, 4, 5] [3, 4, 4]Multi-task Learning with Gradient Communication
  701. 5.33, 1.25, [5, 4, 7] [3, 3, 4]Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
  702. 5.33, 1.25, [4, 7, 5] [4, 4, 4]DHER: Hindsight Experience Replay for Dynamic Goals
  703. 5.33, 1.25, [5, 7, 4] [3, 4, 4]I Know the Feeling: Learning to Converse with Empathy
  704. 5.33, 0.47, [6, 5, 5] [3, 4, 4]Towards Decomposed Linguistic Representation with Holographic Reduced Representation
  705. 5.33, 2.05, [5, 3, 8] [4, 5, 4]Heated-Up Softmax Embedding
  706. 5.33, 1.89, [8, 4, 4] [2, 4, 4]Advocacy Learning
  707. 5.33, 1.25, [4, 7, 5] [4, 4, 3]A Modern Take on the Bias-Variance Tradeoff in Neural Networks
  708. 5.33, 0.47, [6, 5, 5] [2, 4, 3]Surprising Negative Results for Generative Adversarial Tree Search
  709. 5.33, 0.47, [5, 5, 6] [5, 3, 5]Exploring the interpretability of LSTM neural networks over multi-variable data
  710. 5.33, 0.94, [6, 6, 4] [4, 4, 3]Probabilistic Federated Neural Matching
  711. 5.33, 0.47, [5, 5, 6] [3, 3, 4]Importance Resampling for Off-policy Policy Evaluation
  712. 5.33, 0.47, [6, 5, 5] [1, 3, 5]Deep Imitative Models for Flexible Inference, Planning, and Control
  713. 5.33, 0.47, [6, 5, 5] [4, 4, 3]Complementary-label learning for arbitrary losses and models
  714. 5.33, 0.47, [5, 5, 6] [4, 4, 4]Online Hyperparameter Adaptation via Amortized Proximal Optimization
  715. 5.33, 0.47, [6, 5, 5] [4, 4, 2]DEEP GRAPH TRANSLATION
  716. 5.33, 0.94, [6, 6, 4] [3, 4, 5]Adapting Auxiliary Losses Using Gradient Similarity
  717. 5.33, 3.09, [7, 8, 1] [4, 4, 3]Optimal Control Via Neural Networks: A Convex Approach
  718. 5.33, 1.25, [5, 7, 4] [4, 3, 3]Composing Entropic Policies using Divergence Correction
  719. 5.33, 1.25, [5, 7, 4] [3, 4, 3]Neural Predictive Belief Representations
  720. 5.33, 0.47, [5, 5, 6] [3, 4, 4]Learning Backpropagation-Free Deep Architectures with Kernels
  721. 5.33, 0.94, [4, 6, 6] [4, 4, 4]Can I trust you more? Model-Agnostic Hierarchical Explanations
  722. 5.33, 0.47, [5, 6, 5] [3, 4, 5]Open Loop Hyperparameter Optimization and Determinantal Point Processes
  723. 5.33, 1.25, [4, 5, 7] [4, 4, 3]Sorting out Lipschitz function approximation
  724. 5.33, 0.47, [5, 5, 6] [5, 3, 4]Knows When it Doesn’t Know: Deep Abstaining Classifiers
  725. 5.33, 0.47, [5, 6, 5] [3, 3, 2]Probabilistic Knowledge Graph Embeddings
  726. 5.33, 0.47, [5, 6, 5] [4, 3, 3]An Active Learning Framework for Efficient Robust Policy Search
  727. 5.33, 0.47, [5, 5, 6] [4, 2, 2]Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
  728. 5.33, 0.47, [6, 5, 5] [2, 4, 3]Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis
  729. 5.33, 0.47, [5, 6, 5] [5, 3, 4]Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
  730. 5.33, 0.94, [4, 6, 6] [4, 3, 4]Meta-Learning Language-Guided Policy Learning
  731. 5.33, 0.47, [5, 6, 5] [5, 3, 4]Neural Model-Based Reinforcement Learning for Recommendation
  732. 5.33, 0.47, [5, 6, 5] [3, 3, 3]MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy
  733. 5.33, 0.94, [4, 6, 6] [4, 3, 4]IB-GAN: Disentangled Representation Learning with Information Bottleneck GAN
  734. 5.33, 0.47, [5, 5, 6] [4, 2, 5]AntMan: Sparse Low-Rank Compression To Accelerate RNN Inference
  735. 5.33, 0.94, [4, 6, 6] [4, 2, 4]Multi-Agent Dual Learning
  736. 5.33, 1.25, [4, 7, 5] [4, 4, 4]Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks
  737. 5.33, 0.47, [6, 5, 5] [3, 4, 3]Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
  738. 5.33, 0.94, [4, 6, 6] [5, 3, 3]Simple Black-box Adversarial Attacks
  739. 5.33, 0.47, [5, 5, 6] [4, 4, 4]Interpolation-Prediction Networks for Irregularly Sampled Time Series
  740. 5.33, 1.25, [4, 7, 5] [4, 5, 4]SynonymNet: Multi-context Bilateral Matching for Entity Synonyms
  741. 5.33, 1.25, [4, 5, 7] [2, 5, 3]Synthetic Datasets for Neural Program Synthesis
  742. 5.33, 0.94, [4, 6, 6] [4, 3, 3]Generative Adversarial Networks for Extreme Learned Image Compression
  743. 5.33, 0.47, [5, 6, 5] [4, 4, 5]Local Binary Pattern Networks for Character Recognition
  744. 5.25, 1.30, [7, 4, 6, 4] [2, 4, 3, 4]Unified recurrent network for many feature types
  745. 5.25, 0.43, [5, 5, 6, 5] [5, 5, 5, 4]Sample Efficient Imitation Learning for Continuous Control
  746. 5.25, 1.09, [4, 7, 5, 5] [5, 3, 4, 3]Improving Generative Adversarial Imitation Learning with Non-expert Demonstrations
  747. 5.25, 0.83, [6, 5, 6, 4] [3, 3, 3, 4]Generative Feature Matching Networks
  748. 5.25, 0.83, [6, 5, 6, 4] [4, 4, 4, 3]Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective
  749. 5.25, 0.83, [4, 5, 6, 6] [4, 4, 2, 4]Optimistic Acceleration for Optimization
  750. 5.25, 1.09, [5, 5, 7, 4] [5, 3, 4, 4]P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions
  751. 5.00, 0.82, [6, 4, 5] [3, 4, 4]Towards Language Agnostic Universal Representations
  752. 5.00, 1.63, [3, 5, 7] [3, 4, 3]Transfer Learning for Estimating Causal Effects Using Neural Networks
  753. 5.00, 1.63, [7, 3, 5] [4, 5, 4]Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction
  754. 5.00, 1.41, [7, 4, 4] [3, 4, 4]Metric-Optimized Example Weights
  755. 5.00, 0.00, [5, 5, 5] [4, 4, 3]Quantization for Rapid Deployment of Deep Neural Networks
  756. 5.00, 0.00, [5, 5, 5] [4, 3, 4]Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
  757. 5.00, 0.00, [5, 5, 5] [3, 4, 4]Convergence Properties of Deep Neural Networks on Separable Data
  758. 5.00, 1.41, [4, 4, 7] [4, 3, 4]k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks
  759. 5.00, 1.41, [4, 4, 7] [4, 3, 4]Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
  760. 5.00, 0.82, [5, 4, 6] [5, 4, 4]Collapse of deep and narrow neural nets
  761. 5.00, 0.82, [6, 5, 4] [4, 4, 2]Déjà Vu: An Empirical Evaluation of the Memorization Properties of Convnets
  762. 5.00, 1.41, [7, 4, 4] [4, 5, 3]Adversarial Reprogramming of Neural Networks
  763. 5.00, 0.82, [6, 4, 5] [4, 4, 4]Spread Divergences
  764. 5.00, 0.82, [5, 4, 6] [4, 4, 4]Massively Parallel Hyperparameter Tuning
  765. 5.00, 0.82, [4, 5, 6] [4, 3, 3]Using Ontologies To Improve Performance In Massively Multi-label Prediction
  766. 5.00, 0.82, [4, 6, 5] [4, 5, 4]FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE
  767. 5.00, 0.82, [6, 4, 5] [4, 3, 3]Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks
  768. 5.00, 0.00, [5, 5, 5] [4, 5, 4]GRAPH TRANSFORMATION POLICY NETWORK FOR CHEMICAL REACTION PREDICTION
  769. 5.00, 1.41, [7, 4, 4] [4, 4, 3]Discrete flow posteriors for variational inference in discrete dynamical systems
  770. 5.00, 0.82, [4, 6, 5] [3, 3, 4]Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
  771. 5.00, 0.00, [5, 5, 5] [4, 4, 3]Improving Gaussian mixture latent variable model convergence with Optimal Transport
  772. 5.00, 0.00, [5, 5, 5] [3, 5, 2]Volumetric Convolution: Automatic Representation Learning in Unit Ball
  773. 5.00, 0.82, [4, 5, 6] [3, 3, 3]Directional Analysis of Stochastic Gradient Descent via von Mises-Fisher Distributions in Deep Learning
  774. 5.00, 0.82, [6, 5, 4] [3, 4, 3]Convolutional Neural Networks combined with Runge-Kutta Methods
  775. 5.00, 0.82, [4, 5, 6] [3, 4, 4]Cumulative Saliency based Globally Balanced Filter Pruning For Efficient Convolutional Neural Networks
  776. 5.00, 2.16, [4, 8, 3] [5, 5, 3]Initialized Equilibrium Propagation for Backprop-Free Training
  777. 5.00, 2.16, [2, 6, 7] [5, 4, 4]Variational Smoothing in Recurrent Neural Network Language Models
  778. 5.00, 0.82, [4, 6, 5] [4, 5, 3]An Automatic Operation Batching Strategy for the Backward Propagation of Neural Networks Having Dynamic Computation Graphs
  779. 5.00, 0.82, [5, 6, 4] [4, 2, 3]Low Latency Privacy Preserving Inference
  780. 5.00, 0.82, [4, 6, 5] [4, 3, 5]Optimal margin Distribution Network
  781. 5.00, 0.82, [5, 6, 4] [4, 3, 5]Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection
  782. 5.00, 0.00, [5, 5, 5] [4, 4, 5]Learning Discriminators as Energy Networks in Adversarial Learning
  783. 5.00, 0.00, [5, 5, 5] [4, 4, 4]S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
  784. 5.00, 0.00, [5, 5, 5] [4, 4, 3]RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
  785. 5.00, 1.41, [4, 4, 7] [3, 4, 4]Accelerated Value Iteration via Anderson Mixing
  786. 5.00, 0.82, [6, 5, 4] [4, 4, 4]On the Relationship between Neural Machine Translation and Word Alignment
  787. 5.00, 0.82, [5, 6, 4] [4, 4, 4]Denoise while Aggregating: Collaborative Learning in Open-Domain Question Answering
  788. 5.00, 0.82, [6, 5, 4] [4, 4, 5]Unicorn: Continual learning with a universal, off-policy agent
  789. 5.00, 0.82, [5, 6, 4] [4, 3, 5]SnapQuant: A Probabilistic and Nested Parameterization for Binary Networks
  790. 5.00, 0.82, [6, 4, 5] [3, 3, 3]Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
  791. 5.00, 1.63, [7, 3, 5] [4, 4, 4]On Accurate Evaluation of GANs for Language Generation
  792. 5.00, 1.41, [4, 7, 4] [5, 2, 3]Cautious Deep Learning
  793. 5.00, 1.41, [7, 4, 4] [5, 3, 5]A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation
  794. 5.00, 0.82, [4, 6, 5] [4, 3, 4]Likelihood-based Permutation Invariant Loss Function for Probability Distributions
  795. 5.00, 0.82, [5, 4, 6] [4, 4, 3]The Effectiveness of Pre-Trained Code Embeddings
  796. 5.00, 0.82, [4, 5, 6] [4, 4, 3]Unsupervised Document Representation using Partition Word-Vectors Averaging
  797. 5.00, 0.00, [5, 5, 5] [4, 3, 4]Ada-Boundary: Accelerating the DNN Training via Adaptive Boundary Batch Selection
  798. 5.00, 0.82, [6, 4, 5] [4, 4, 4]Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces
  799. 5.00, 0.00, [5, 5, 5] [4, 3, 3]Revisiting Reweighted Wake-Sleep
  800. 5.00, 0.82, [4, 5, 6] [4, 4, 4]Teacher Guided Architecture Search
  801. 5.00, 0.82, [6, 4, 5] [4, 3, 4]What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning
  802. 5.00, 0.82, [5, 6, 4] [4, 3, 5]Connecting the Dots Between MLE and RL for Sequence Generation
  803. 5.00, 0.82, [5, 4, 6] [4, 4, 2]Consistent Jumpy Predictions for Videos and Scenes
  804. 5.00, 0.00, [5, 5, 5] [5, 5, 4]Phrase-Based Attentions
  805. 5.00, 0.82, [5, 6, 4] [4, 3, 5]On-Policy Trust Region Policy Optimisation with Replay Buffers
  806. 5.00, 0.00, [5, 5, 5] [3, 4, 3]Capacity of Deep Neural Networks under Parameter Quantization
  807. 5.00, 1.41, [4, 4, 7] [4, 4, 3]Probabilistic Semantic Embedding
  808. 5.00, 1.41, [4, 4, 7] [4, 3, 3]The Importance of Norm Regularization in Linear Graph Embedding: Theoretical Analysis and Empirical Demonstration
  809. 5.00, 0.00, [5, 5, 5] [3, 4, 3]Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
  810. 5.00, 0.82, [6, 5, 4] [4, 4, 3]Point Cloud GAN
  811. 5.00, 0.82, [5, 4, 6] [4, 4, 5]Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology
  812. 5.00, 0.00, [5, 5, 5] [4, 4, 4]Dataset Distillation
  813. 5.00, 0.82, [6, 4, 5] [2, 4, 4]Representation-Constrained Autoencoders and an Application to Wireless Positioning
  814. 5.00, 0.82, [5, 6, 4] [4, 3, 5]The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects
  815. 5.00, 0.82, [6, 4, 5] [4, 5, 5]A Case for Object Compositionality in Deep Generative Models of Images
  816. 5.00, 0.00, [5, 5, 5] [5, 4, 3]An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack
  817. 5.00, 0.82, [5, 4, 6] [4, 4, 3]COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
  818. 5.00, 0.00, [5, 5, 5] [2, 4, 4]Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation
  819. 5.00, 0.00, [5, 5, 5] [4, 3, 3]Transferrable End-to-End Learning for Protein Interface Prediction
  820. 5.00, 1.41, [3, 6, 6] [3, 3, 1]Improved robustness to adversarial examples using Lipschitz regularization of the loss
  821. 5.00, 0.00, [5, 5, 5] [5, 4, 3]Dense Morphological Network: An Universal Function Approximator
  822. 5.00, 0.00, [5, 5, 5] [5, 2, 5]High Resolution and Fast Face Completion via Progressively Attentive GANs
  823. 5.00, 0.00, [5, 5, 5] [1, 3, 3]Model Comparison for Semantic Grouping
  824. 5.00, 0.82, [4, 6, 5] [4, 3, 4]Learning to Refer to 3D Objects with Natural Language
  825. 5.00, 0.82, [4, 5, 6] [4, 3, 4]Dissecting an Adversarial framework for Information Retrieval
  826. 5.00, 0.82, [4, 6, 5] [4, 3, 5]NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES
  827. 5.00, 1.41, [7, 4, 4] [4, 4, 5]On Learning Heteroscedastic Noise Models within Differentiable Bayes Filters
  828. 5.00, 0.82, [4, 5, 6] [4, 4, 5]Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models
  829. 5.00, 0.00, [5, 5, 5] [5, 4, 4]A PRIVACY-PRESERVING IMAGE CLASSIFICATION FRAMEWORK WITH A LEARNABLE OBFUSCATOR
  830. 5.00, 0.00, [5, 5, 5] [4, 4, 4]Learning with Random Learning Rates.
  831. 5.00, 0.82, [4, 6, 5] [5, 4, 5]Canonical Correlation Analysis with Implicit Distributions
  832. 5.00, 1.63, [3, 5, 7] [3, 4, 5]Guided Exploration in Deep Reinforcement Learning
  833. 5.00, 1.41, [7, 4, 4] [4, 2, 3]The GAN Landscape: Losses, Architectures, Regularization, and Normalization
  834. 5.00, 1.63, [5, 3, 7] [3, 4, 3]TherML: The Thermodynamics of Machine Learning
  835. 5.00, 0.82, [4, 5, 6] [4, 5, 3]Graph2Seq: Scalable Learning Dynamics for Graphs
  836. 5.00, 0.82, [5, 6, 4] [5, 4, 4]ChoiceNet: Robust Learning by Revealing Output Correlations
  837. 5.00, 1.41, [7, 4, 4] [5, 4, 4]N-Ary Quantization for CNN Model Compression and Inference Acceleration
  838. 5.00, 0.82, [5, 6, 4] [2, 4, 2]Automata Guided Skill Composition
  839. 5.00, 0.82, [5, 6, 4] [5, 2, 5]Learning To Plan
  840. 5.00, 2.16, [6, 7, 2] [3, 4, 3]Implicit Autoencoders
  841. 5.00, 0.82, [4, 6, 5] [5, 4, 4]COCO-GAN: Conditional Coordinate Generative Adversarial Network
  842. 5.00, 0.82, [6, 4, 5] [5, 2, 4]Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation
  843. 5.00, 1.41, [7, 4, 4] [3, 4, 3]Generative Ensembles for Robust Anomaly Detection
  844. 5.00, 0.00, [5, 5, 5] [3, 3, 5]Characterizing Malicious Edges targeting on Graph Neural Networks
  845. 5.00, 0.82, [5, 6, 4] [4, 3, 5]Zero-shot Dual Machine Translation
  846. 5.00, 0.00, [5, 5, 5] [4, 3, 4]Inferring Reward Functions from Demonstrators with Unknown Biases
  847. 5.00, 0.00, [5, 5, 5] [3, 4, 4]A comprehensive, application-oriented study of catastrophic forgetting in DNNs
  848. 5.00, 0.82, [5, 6, 4] [4, 4, 5]Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries
  849. 5.00, 2.16, [2, 7, 6] [4, 3, 3]RANDOM MASK: Towards Robust Convolutional Neural Networks
  850. 5.00, 0.00, [5, 5, 5] [4, 5, 5]Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
  851. 5.00, 0.82, [6, 4, 5] [4, 4, 2]Label Propagation Networks
  852. 5.00, 1.63, [5, 3, 7] [3, 4, 2]Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
  853. 5.00, 0.82, [6, 4, 5] [4, 5, 5]Learning Global Additive Explanations for Neural Nets Using Model Distillation
  854. 5.00, 0.82, [6, 5, 4] [3, 3, 4]Understand the dynamics of GANs via Primal-Dual Optimization
  855. 5.00, 0.82, [6, 4, 5] [4, 4, 4]Rethinking learning rate schedules for stochastic optimization
  856. 5.00, 1.41, [4, 7, 4] [4, 3, 4]Learning and Planning with a Semantic Model
  857. 5.00, 0.00, [5, 5, 5] [3, 4, 3]Metropolis-Hastings view on variational inference and adversarial training
  858. 5.00, 0.82, [6, 5, 4] [4, 5, 4]Learning To Simulate
  859. 5.00, 1.41, [3, 6, 6] [5, 4, 4]Graph2Seq: Graph to Sequence Learning with Attention-Based Neural Networks
  860. 5.00, 0.82, [4, 6, 5] [3, 3, 4]Information Regularized Neural Networks
  861. 5.00, 0.82, [5, 4, 6] [4, 4, 3]Transfer Learning for Sequences via Learning to Collocate
  862. 5.00, 0.82, [6, 4, 5] [4, 3, 3]Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search
  863. 5.00, 0.82, [5, 6, 4] [5, 4, 3]Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
  864. 5.00, 0.82, [4, 6, 5] [4, 4, 4]Learning Actionable Representations with Goal Conditioned Policies
  865. 5.00, 1.41, [7, 4, 4] [3, 4, 2]Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning
  866. 5.00, 1.41, [4, 4, 7] [4, 4, 4]A RECURRENT NEURAL CASCADE-BASED MODEL FOR CONTINUOUS-TIME DIFFUSION PROCESS
  867. 5.00, 0.00, [5, 5, 5] [4, 4, 4]ON THE EFFECTIVENESS OF TASK GRANULARITY FOR TRANSFER LEARNING
  868. 5.00, 1.41, [4, 4, 7] [5, 3, 3]NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK
  869. 5.00, 0.82, [5, 6, 4] [4, 4, 5]Dynamic Graph Representation Learning via Self-Attention Networks
  870. 5.00, 0.00, [5, 5, 5] [4, 4, 3]Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning
  871. 5.00, 0.00, [5, 5, 5] [5, 5, 4]VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Short Learning
  872. 5.00, 1.63, [3, 5, 7] [4, 3, 2]Noisy Information Bottlenecks for Generalization
  873. 5.00, 0.00, [5, 5, 5] [4, 5, 4]Learning Diverse Generations using Determinantal Point Processes
  874. 5.00, 0.00, [5, 5, 5] [4, 3, 4]Learning Representations of Categorical Feature Combinations via Self-Attention
  875. 5.00, 0.82, [4, 6, 5] [4, 4, 5]MLPrune: Multi-Layer Pruning for Automated Neural Network Compression
  876. 5.00, 1.41, [4, 4, 7] [4, 4, 4]Zero-shot Learning for Speech Recognition with Universal Phonetic Model
  877. 5.00, 0.82, [4, 5, 6] [4, 5, 2]Reinforced Imitation Learning from Observations
  878. 5.00, 0.82, [4, 5, 6] [5, 4, 2]Link Prediction in Hypergraphs using Graph Convolutional Networks
  879. 5.00, 0.82, [4, 6, 5] [5, 3, 4]Structured Content Preservation for Unsupervised Text Style Transfer
  880. 5.00, 0.00, [5, 5, 5] [2, 3, 5]Riemannian TransE: Multi-relational Graph Embedding in Non-Euclidean Space
  881. 5.00, 0.82, [6, 4, 5] [2, 4, 3]On Regularization and Robustness of Deep Neural Networks
  882. 5.00, 0.82, [6, 5, 4] [3, 3, 3]Scalable Neural Theorem Proving on Knowledge Bases and Natural Language
  883. 5.00, 2.16, [8, 3, 4] [5, 5, 5]Learning to remember: Dynamic Generative Memory for Continual Learning
  884. 5.00, 0.82, [6, 4, 5] [4, 4, 4]A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
  885. 5.00, 0.82, [5, 4, 6] [5, 4, 3]Human-Guided Column Networks: Augmenting Deep Learning with Advice
  886. 5.00, 0.82, [4, 6, 5] [5, 2, 4]Double Neural Counterfactual Regret Minimization
  887. 5.00, 0.82, [4, 5, 6] [3, 3, 4]Transferring SLU Models in Novel Domains
  888. 5.00, 2.16, [3, 4, 8] [5, 5, 4]Analysis of Memory Organization for Dynamic Neural Networks
  889. 5.00, 0.82, [6, 5, 4] [5, 3, 4]Systematic Generalization: What Is Required and Can It Be Learned?
  890. 5.00, 0.82, [5, 6, 4] [4, 4, 4]Context Mover’s Distance & Barycenters: Optimal transport of contexts for building representations
  891. 5.00, 1.22, [6, 5, 6, 3] [3, 3, 3, 5]Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
  892. 5.00, 0.82, [4, 6, 5] [5, 4, 4]Successor Options : An Option Discovery Algorithm for Reinforcement Learning
  893. 5.00, 0.82, [4, 5, 6] [4, 3, 5]STCN: Stochastic Temporal Convolutional Networks
  894. 5.00, 0.82, [6, 4, 5] [4, 5, 4]Analyzing Federated Learning through an Adversarial Lens
  895. 5.00, 1.22, [7, 4, 4, 5] [4, 4, 3, 4]Causal Reasoning from Meta-learning
  896. 5.00, 0.82, [6, 4, 5] [4, 3, 4]AD-VAT: An Asymmetric Dueling mechanism for learning Visual Active Tracking
  897. 5.00, 0.00, [5, 5, 5] [4, 3, 5]Incremental Few-Shot Learning with Attention Attractor Networks
  898. 5.00, 0.82, [6, 5, 4] [4, 4, 3]GenEval: A Benchmark Suite for Evaluating Generative Models
  899. 5.00, 0.82, [4, 5, 6] [5, 5, 3]Approximation capability of neural networks on sets of probability measures and tree-structured data
  900. 5.00, 0.82, [4, 6, 5] [3, 3, 4]Robustness Certification with Refinement
  901. 5.00, 0.82, [6, 4, 5] [3, 5, 3]Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
  902. 5.00, 0.00, [5, 5, 5] [4, 4, 4]Making Convolutional Networks Shift-Invariant Again
  903. 5.00, 0.82, [6, 5, 4] [4, 4, 4]Adversarial Audio Super-Resolution with Unsupervised Feature Losses
  904. 5.00, 1.63, [3, 7, 5] [4, 5, 4]ACTRCE: Augmenting Experience via Teacher’s Advice
  905. 5.00, 0.00, [5, 5, 5] [3, 4, 3]Learnable Embedding Space for Efficient Neural Architecture Compression
  906. 5.00, 1.41, [4, 7, 4] [5, 4, 3]ISA-VAE: Independent Subspace Analysis with Variational Autoencoders
  907. 5.00, 0.82, [6, 5, 4] [3, 3, 4]Interpretable Continual Learning
  908. 5.00, 0.00, [5, 5, 5] [5, 4, 5]Experience replay for continual learning
  909. 5.00, 0.82, [6, 5, 4] [4, 3, 4]Accelerated Gradient Flow for Probability Distributions
  910. 5.00, 0.00, [5, 5, 5] [3, 3, 3]Learning to Progressively Plan
  911. 5.00, 0.82, [5, 4, 6] [4, 4, 4]Capsules Graph Neural Network
  912. 5.00, 0.82, [5, 4, 6] [4, 4, 5]Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
  913. 5.00, 0.82, [5, 6, 4] [3, 4, 3]Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification
  914. 5.00, 0.82, [5, 6, 4] [2, 4, 4]Graph2Graph Networks for Multi-Label Classification
  915. 5.00, 1.41, [3, 6, 6] [4, 4, 4]Towards GAN Benchmarks Which Require Generalization
  916. 5.00, 0.71, [5, 6, 4, 5] [5, 3, 5, 5]TTS-GAN: a generative adversarial network for style modeling in a text-to-speech system
  917. 5.00, 1.22, [3, 6, 5, 6] [4, 3, 4, 3]A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs
  918. 5.00, 0.82, [5, 4, 6] [5, 4, 4]Local Image-to-Image Translation via Pixel-wise Highway Adaptive Instance Normalization
  919. 5.00, 0.82, [4, 6, 5] [4, 4, 5]INFORMATION MAXIMIZATION AUTO-ENCODING
  920. 5.00, 0.82, [4, 6, 5] [4, 5, 5]Generative Adversarial Self-Imitation Learning
  921. 5.00, 1.41, [7, 4, 4] [3, 3, 3]Generative Adversarial Models for Learning Private and Fair Representations
  922. 4.80, 1.17, [6, 3, 4, 6, 5] [5, 4, 4, 2, 4]Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
  923. 4.75, 0.83, [6, 4, 4, 5] [3, 2, 3, 3]Cutting Down Training Memory by Re-fowarding
  924. 4.75, 0.83, [5, 6, 4, 4] [5, 4, 4, 4]Multi-turn Dialogue Response Generation in an Adversarial Learning Framework
  925. 4.75, 0.43, [5, 5, 4, 5] [2, 2, 4, 5]Pooling Is Neither Necessary nor Sufficient for Appropriate Deformation Stability in CNNs
  926. 4.75, 1.92, [8, 3, 4, 4] [2, 5, 5, 4]Geomstats: a Python Package for Riemannian Geometry in Machine Learning
  927. 4.75, 1.48, [4, 3, 7, 5] [3, 4, 4, 4]Towards a better understanding of Vector Quantized Autoencoders
  928. 4.67, 0.47, [4, 5, 5] [2, 3, 3]Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems
  929. 4.67, 1.70, [7, 3, 4] [3, 5, 4]CHEMICAL NAMES STANDARDIZATION USING NEURAL SEQUENCE TO SEQUENCE MODEL
  930. 4.67, 0.94, [6, 4, 4] [5, 1, 4]Traditional and Heavy Tailed Self Regularization in Neural Network Models
  931. 4.67, 0.47, [4, 5, 5] [3, 2, 4]Count-Based Exploration with the Successor Representation
  932. 4.67, 0.47, [5, 5, 4] [3, 4, 4]Learning Graph Representations by Dendrograms
  933. 4.67, 0.47, [5, 4, 5] [2, 3, 4]Efficient Dictionary Learning with Gradient Descent
  934. 4.67, 1.25, [3, 6, 5] [5, 2, 5]$A^*$ sampling with probability matching
  935. 4.67, 0.47, [4, 5, 5] [3, 5, 3]Neural Variational Inference For Embedding Knowledge Graphs
  936. 4.67, 0.94, [4, 4, 6] [4, 4, 4]SupportNet: solving catastrophic forgetting in class incremental learning with support data
  937. 4.67, 0.94, [4, 6, 4] [4, 5, 4]Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
  938. 4.67, 1.70, [7, 3, 4] [4, 5, 3]Unsupervised Word Discovery with Segmental Neural Language Models
  939. 4.67, 1.25, [6, 3, 5] [4, 5, 4]Generative Adversarial Network Training is a Continual Learning Problem
  940. 4.67, 1.70, [7, 4, 3] [4, 3, 4]GENERALIZED ADAPTIVE MOMENT ESTIMATION
  941. 4.67, 0.47, [5, 5, 4] [5, 3, 3]Effective and Efficient Batch Normalization Using Few Uncorrelated Data for Statistics’ Estimation
  942. 4.67, 0.94, [4, 6, 4] [4, 5, 4]TequilaGAN: How To Easily Identify GAN Samples
  943. 4.67, 0.94, [6, 4, 4] [4, 4, 3]Gradient Descent Happens in a Tiny Subspace
  944. 4.67, 1.25, [5, 6, 3] [4, 4, 4]Dual Skew Divergence Loss for Neural Machine Translation
  945. 4.67, 0.47, [4, 5, 5] [3, 4, 3]Stochastic Learning of Additive Second-Order Penalties with Applications to Fairness
  946. 4.67, 0.94, [6, 4, 4] [5, 4, 4]Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
  947. 4.67, 0.94, [4, 4, 6] [4, 4, 4]Boosting Trust Region Policy Optimization by Normalizing flows Policy
  948. 4.67, 0.47, [4, 5, 5] [4, 3, 4]Backplay: ‘Man muss immer umkehren’
  949. 4.67, 0.94, [4, 4, 6] [4, 4, 4]HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS
  950. 4.67, 0.94, [6, 4, 4] [4, 3, 4]Improved resistance of neural networks to adversarial images through generative pre-training
  951. 4.67, 0.47, [4, 5, 5] [4, 5, 3]Context-aware Forecasting for Multivariate Stationary Time-series
  952. 4.67, 0.94, [4, 6, 4] [4, 4, 5]Selective Self-Training for semi-supervised Learning
  953. 4.67, 0.94, [4, 4, 6] [5, 3, 4]Learning with Little Data: Evaluation of Deep Learning Algorithms
  954. 4.67, 1.70, [7, 3, 4] [5, 4, 4]What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation
  955. 4.67, 0.94, [6, 4, 4] [3, 4, 4]Improving latent variable descriptiveness by modelling rather than ad-hoc factors
  956. 4.67, 0.47, [5, 5, 4] [4, 3, 4]Conditional Network Embeddings
  957. 4.67, 1.70, [7, 3, 4] [3, 4, 3]Holographic and other Point Set Distances for Machine Learning
  958. 4.67, 0.94, [6, 4, 4] [3, 3, 4]Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction
  959. 4.67, 0.47, [4, 5, 5] [5, 4, 4]PRUNING IN TRAINING: LEARNING AND RANKING SPARSE CONNECTIONS IN DEEP CONVOLUTIONAL NETWORKS
  960. 4.67, 0.47, [4, 5, 5] [4, 5, 4]RelWalk — A Latent Variable Model Approach to Knowledge Graph Embedding
  961. 4.67, 0.47, [5, 5, 4] [2, 3, 4]Unsupervised Expectation Learning for Multisensory Binding
  962. 4.67, 1.25, [6, 5, 3] [4, 4, 4]Sentence Encoding with Tree-Constrained Relation Networks
  963. 4.67, 0.47, [4, 5, 5] [3, 2, 3]Pushing the bounds of dropout
  964. 4.67, 0.94, [4, 6, 4] [4, 5, 4]StrokeNet: A Neural Painting Environment
  965. 4.67, 1.25, [5, 6, 3] [2, 2, 4]Intriguing Properties of Learned Representations
  966. 4.67, 1.25, [5, 3, 6] [4, 4, 4]Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication
  967. 4.67, 0.47, [5, 5, 4] [4, 4, 4]Computation-Efficient Quantization Method for Deep Neural Networks
  968. 4.67, 1.25, [3, 5, 6] [4, 5, 3]Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels
  969. 4.67, 0.47, [5, 5, 4] [4, 3, 4]Consistency-based anomaly detection with adaptive multiple-hypotheses predictions
  970. 4.67, 1.25, [5, 6, 3] [2, 4, 5]Integrated Steganography and Steganalysis with Generative Adversarial Networks
  971. 4.67, 0.47, [4, 5, 5] [4, 4, 5]Rectified Gradient: Layer-wise Thresholding for Sharp and Coherent Attribution Maps
  972. 4.67, 0.94, [6, 4, 4] [5, 4, 4]Generative replay with feedback connections as a general strategy for continual learning
  973. 4.67, 1.25, [6, 5, 3] [2, 3, 4]Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
  974. 4.67, 0.94, [6, 4, 4] [5, 3, 4]Effective Path: Know the Unknowns of Neural Network
  975. 4.67, 1.25, [3, 6, 5] [4, 4, 4]Siamese Capsule Networks
  976. 4.67, 0.47, [4, 5, 5] [5, 3, 4]Ergodic Measure Preserving Flows
  977. 4.67, 1.25, [5, 3, 6] [5, 5, 4]3D-RelNet: Joint Object and Relational Network for 3D Prediction
  978. 4.67, 0.47, [5, 5, 4] [4, 5, 4]Finding Mixed Nash Equilibria of Generative Adversarial Networks
  979. 4.67, 0.47, [5, 4, 5] [5, 5, 4]Investigating CNNs’ Learning Representation under label noise
  980. 4.67, 0.94, [4, 6, 4] [5, 4, 4]Conscious Inference for Object Detection
  981. 4.67, 0.47, [5, 4, 5] [4, 2, 3]Learning Information Propagation in the Dynamical Systems via Information Bottleneck Hierarchy
  982. 4.67, 0.47, [5, 4, 5] [2, 5, 4]TabNN: A Universal Neural Network Solution for Tabular Data
  983. 4.67, 1.25, [3, 5, 6] [3, 2, 4]Probabilistic Binary Neural Networks
  984. 4.67, 0.47, [5, 4, 5] [5, 4, 3]Gradient-based learning for F-measure and other performance metrics
  985. 4.67, 0.47, [4, 5, 5] [4, 5, 2]SEGEN: SAMPLE-ENSEMBLE GENETIC EVOLUTIONARY NETWORK MODEL
  986. 4.67, 0.94, [6, 4, 4] [4, 4, 4]Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
  987. 4.67, 1.25, [5, 6, 3] [4, 4, 4]Learning to Drive by Observing the Best and Synthesizing the Worst
  988. 4.67, 1.89, [6, 2, 6] [5, 5, 3]Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning
  989. 4.67, 1.25, [3, 6, 5] [3, 4, 3]MARGINALIZED AVERAGE ATTENTIONAL NETWORK FOR WEAKLY-SUPERVISED LEARNING
  990. 4.67, 1.70, [3, 7, 4] [3, 5, 4]Discriminative out-of-distribution detection for semantic segmentation
  991. 4.67, 0.47, [5, 5, 4] [4, 4, 3]Integral Pruning on Activations and Weights for Efficient Neural Networks
  992. 4.67, 0.47, [4, 5, 5] [4, 4, 4]Online Bellman Residue Minimization via Saddle Point Optimization
  993. 4.67, 0.47, [5, 4, 5] [4, 5, 4]Area Attention
  994. 4.67, 0.47, [5, 4, 5] [2, 3, 2]NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING
  995. 4.67, 0.47, [5, 5, 4] [4, 5, 4]Variational Sparse Coding
  996. 4.67, 0.94, [6, 4, 4] [5, 3, 4]What Information Does a ResNet Compress?
  997. 4.67, 1.25, [5, 6, 3] [5, 4, 5]Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
  998. 4.67, 0.47, [5, 5, 4] [4, 3, 4]LIT: Block-wise Intermediate Representation Training for Model Compression
  999. 4.67, 0.47, [5, 4, 5] [5, 4, 4]An Energy-Based Framework for Arbitrary Label Noise Correction
  1000. 4.67, 0.94, [6, 4, 4] [1, 3, 2]ACE: Artificial Checkerboard Enhancer to Induce and Evade Adversarial Attacks
  1001. 4.67, 0.47, [4, 5, 5] [4, 3, 4]SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
  1002. 4.67, 0.94, [6, 4, 4] [4, 5, 4]Differentiable Expected BLEU for Text Generation
  1003. 4.67, 0.47, [4, 5, 5] [4, 4, 4]Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching
  1004. 4.67, 1.25, [6, 3, 5] [4, 4, 3]Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning
  1005. 4.67, 0.47, [4, 5, 5] [4, 3, 4]Sufficient Conditions for Robustness to Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks
  1006. 4.67, 0.47, [4, 5, 5] [5, 4, 3]Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
  1007. 4.67, 0.47, [5, 5, 4] [4, 4, 3]PAIRWISE AUGMENTED GANS WITH ADVERSARIAL RECONSTRUCTION LOSS
  1008. 4.67, 0.47, [5, 5, 4] [4, 3, 5]Learned optimizers that outperform on wall-clock and validation loss
  1009. 4.67, 0.94, [4, 6, 4] [4, 4, 5]Stability of Stochastic Gradient Method with Momentum for Strongly Convex Loss Functions
  1010. 4.67, 0.47, [5, 4, 5] [4, 3, 5]When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models?
  1011. 4.67, 0.47, [5, 4, 5] [4, 5, 3]Convergence Guarantees for RMSProp and ADAM in Non-Convex Optimization and an Empirical Comparison to Nesterov Acceleration
  1012. 4.67, 0.94, [4, 6, 4] [5, 4, 4]Geometry aware convolutional filters for omnidirectional images representation
  1013. 4.67, 0.94, [6, 4, 4] [2, 3, 5]FEATURE PRIORITIZATION AND REGULARIZATION IMPROVE STANDARD ACCURACY AND ADVERSARIAL ROBUSTNESS
  1014. 4.67, 0.47, [4, 5, 5] [4, 5, 3]Learning Gibbs-regularized GANs with variational discriminator reparameterization
  1015. 4.67, 0.47, [5, 4, 5] [4, 4, 2]Neural separation of observed and unobserved distributions
  1016. 4.67, 0.47, [5, 4, 5] [3, 3, 3]Penetrating the Fog: the Path to Efficient CNN Models
  1017. 4.67, 0.94, [4, 4, 6] [4, 3, 4]Expressiveness in Deep Reinforcement Learning
  1018. 4.67, 0.47, [4, 5, 5] [5, 4, 4]Generating Realistic Stock Market Order Streams
  1019. 4.67, 0.47, [4, 5, 5] [5, 3, 4]An investigation of model-free planning
  1020. 4.67, 1.25, [3, 6, 5] [5, 3, 3]Selectivity metrics can overestimate the selectivity of units: a case study on AlexNet
  1021. 4.67, 0.47, [5, 5, 4] [4, 3, 4]CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks
  1022. 4.67, 0.47, [5, 5, 4] [3, 5, 5]Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks
  1023. 4.67, 0.94, [6, 4, 4] [4, 4, 4]Sliced Wasserstein Auto-Encoders
  1024. 4.67, 1.25, [5, 3, 6] [4, 3, 5]End-to-end learning of pharmacological assays from high-resolution microscopy images
  1025. 4.67, 1.25, [3, 5, 6] [4, 3, 4]Safe Policy Learning from Observations
  1026. 4.67, 0.94, [4, 4, 6] [4, 4, 3]A Study of Robustness of Neural Nets Using Approximate Feature Collisions
  1027. 4.67, 0.47, [5, 5, 4] [4, 3, 3]SSoC: Learning Spontaneous and Self-Organizing Communication for Multi-Agent Collaboration
  1028. 4.67, 1.25, [6, 3, 5] [4, 4, 3]On the Geometry of Adversarial Examples
  1029. 4.67, 0.47, [4, 5, 5] [4, 3, 3]Neural Networks with Structural Resistance to Adversarial Attacks
  1030. 4.67, 0.47, [5, 4, 5] [4, 4, 4]Partially Mutual Exclusive Softmax for Positive and Unlabeled data
  1031. 4.67, 1.25, [3, 5, 6] [4, 4, 4]Unsupervised Disentangling Structure and Appearance
  1032. 4.67, 0.47, [4, 5, 5] [4, 4, 4]Success at any cost: value constrained model-free continuous control
  1033. 4.67, 0.47, [5, 4, 5] [4, 4, 3]Predictive Uncertainty through Quantization
  1034. 4.67, 0.94, [6, 4, 4] [4, 5, 5]Maximum a Posteriori on a Submanifold: a General Image Restoration Method with GAN
  1035. 4.67, 0.47, [5, 4, 5] [4, 4, 4]Zero-training Sentence Embedding via Orthogonal Basis
  1036. 4.67, 0.47, [5, 4, 5] [4, 4, 4]The Expressive Power of Gated Recurrent Units as a Continuous Dynamical System
  1037. 4.67, 0.94, [4, 4, 6] [4, 5, 3]SIMILE: Introducing Sequential Information towards More Effective Imitation Learning
  1038. 4.67, 2.05, [7, 2, 5] [4, 5, 3]Meta-learning with differentiable closed-form solvers
  1039. 4.67, 1.25, [3, 6, 5] [4, 4, 4]Mode Normalization
  1040. 4.67, 0.94, [4, 6, 4] [2, 4, 4]Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks
  1041. 4.67, 1.25, [3, 5, 6] [4, 3, 4]NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search
  1042. 4.67, 1.70, [4, 7, 3] [3, 4, 4]A theoretical framework for deep and locally connected ReLU network
  1043. 4.67, 0.94, [4, 6, 4] [3, 3, 4]Approximation and non-parametric estimation of ResNet-type convolutional neural networks via block-sparse fully-connected neural networks
  1044. 4.67, 0.47, [5, 5, 4] [4, 3, 5]Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
  1045. 4.67, 1.25, [3, 6, 5] [1, 4, 4]Pix2Scene: Learning Implicit 3D Representations from Images
  1046. 4.67, 0.94, [4, 4, 6] [2, 5, 3]A Proposed Hierarchy of Deep Learning Tasks
  1047. 4.67, 0.47, [5, 4, 5] [5, 5, 4]CGNF: Conditional Graph Neural Fields
  1048. 4.67, 0.94, [6, 4, 4] [4, 4, 3]Self-Supervised Generalisation with Meta Auxiliary Learning
  1049. 4.67, 0.47, [4, 5, 5] [4, 4, 2]Theoretical and Empirical Study of Adversarial Examples
  1050. 4.67, 1.70, [4, 3, 7] [4, 4, 3]Coupled Recurrent Models for Polyphonic Music Composition
  1051. 4.67, 0.94, [4, 6, 4] [3, 3, 4]DEEP-TRIM: REVISITING L1 REGULARIZATION FOR CONNECTION PRUNING OF DEEP NETWORK
  1052. 4.67, 0.47, [5, 4, 5] [2, 4, 3]Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning
  1053. 4.67, 0.47, [5, 5, 4] [4, 4, 4]Model Compression with Generative Adversarial Networks
  1054. 4.67, 1.25, [6, 5, 3] [4, 4, 4]Text Infilling
  1055. 4.67, 1.25, [6, 3, 5] [4, 3, 4]Visual Imitation with a Minimal Adversary
  1056. 4.67, 1.25, [6, 3, 5] [3, 3, 3]Novel positional encodings to enable tree-structured transformers
  1057. 4.67, 0.47, [5, 4, 5] [4, 4, 4]Shaping representations through communication
  1058. 4.67, 0.47, [5, 4, 5] [3, 3, 4]Characterizing Vulnerabilities of Deep Reinforcement Learning
  1059. 4.67, 0.47, [4, 5, 5] [4, 3, 4]Multi-Grained Entity Proposal Network for Named Entity Recognition
  1060. 4.67, 0.47, [5, 5, 4] [3, 4, 4]Measuring Density and Similarity of Task Relevant Information in Neural Representations
  1061. 4.67, 0.47, [5, 5, 4] [4, 3, 4]Outlier Detection from Image Data
  1062. 4.67, 0.47, [5, 5, 4] [4, 3, 5]Accelerated Sparse Recovery Under Structured Measurements
  1063. 4.67, 0.94, [6, 4, 4] [3, 3, 4]Object-Oriented Model Learning through Multi-Level Abstraction
  1064. 4.67, 1.70, [3, 7, 4] [4, 3, 3]Learning to control self-assembling morphologies: a study of generalization via modularity
  1065. 4.67, 0.47, [5, 5, 4] [4, 4, 4]Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets
  1066. 4.67, 0.47, [4, 5, 5] [3, 4, 4]Manifold Alignment via Feature Correspondence
  1067. 4.67, 1.70, [3, 4, 7] [4, 4, 3]Explicit Recall for Efficient Exploration
  1068. 4.67, 0.47, [5, 4, 5] [4, 4, 3]Differential Equation Networks
  1069. 4.67, 0.47, [5, 4, 5] [4, 4, 4]Predicting the Present and Future States of Multi-agent Systems from Partially-observed Visual Data
  1070. 4.67, 0.47, [5, 5, 4] [4, 4, 5]Learning shared manifold representation of images and attributes for generalized zero-shot learning
  1071. 4.67, 0.47, [5, 4, 5] [3, 5, 4]Inference of unobserved event streams with neural Hawkes particle smoothing
  1072. 4.50, 0.50, [5, 4] [3, 3]Improving On-policy Learning with Statistical Reward Accumulation
  1073. 4.50, 0.50, [5, 4, 4, 5] [2, 3, 2, 2]Unification of Recurrent Neural Network Architectures and Quantum Inspired Stable Design
  1074. 4.50, 0.50, [5, 5, 4, 4] [3, 3, 4, 4]One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
  1075. 4.50, 0.50, [4, 5] [4, 4]Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
  1076. 4.50, 0.50, [5, 4, 4, 5] [3, 4, 4, 4]Music Transformer
  1077. 4.40, 0.80, [6, 4, 4, 4, 4] [4, 5, 4, 3, 5]Context Dependent Modulation of Activation Function
  1078. 4.33, 0.47, [5, 4, 4] [4, 4, 2]Unsupervised classification into unknown k classes
  1079. 4.33, 0.47, [4, 4, 5] [4, 5, 4]Adaptive Convolutional ReLUs
  1080. 4.33, 0.47, [4, 4, 5] [4, 3, 3]FEED: Feature-level Ensemble Effect for knowledge Distillation
  1081. 4.33, 1.89, [3, 3, 7] [4, 3, 3]Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
  1082. 4.33, 1.25, [4, 6, 3] [3, 2, 4]Variation Network: Learning High-level Attributes for Controlled Input Manipulation
  1083. 4.33, 0.94, [5, 3, 5] [3, 5, 4]Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
  1084. 4.33, 1.25, [3, 6, 4] [4, 4, 3]Targeted Adversarial Examples for Black Box Audio Systems
  1085. 4.33, 0.47, [4, 4, 5] [4, 4, 4]Neuron Hierarchical Networks
  1086. 4.33, 1.70, [6, 5, 2] [5, 4, 5]Online Learning for Supervised Dimension Reduction
  1087. 4.33, 0.47, [4, 5, 4] [4, 4, 4]Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
  1088. 4.33, 0.47, [4, 4, 5] [4, 4, 3]MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA
  1089. 4.33, 1.25, [4, 6, 3] [2, 3, 4]Unsupervised Meta-Learning for Reinforcement Learning
  1090. 4.33, 1.70, [5, 2, 6] [3, 5, 3]q-Neurons: Neuron Activations based on Stochastic Jackson’s Derivative Operators
  1091. 4.33, 0.47, [5, 4, 4] [4, 4, 4]Learning a Neural-network-based Representation for Open Set Recognition
  1092. 4.33, 0.47, [4, 4, 5] [4, 4, 4]No Pressure! Addressing Problem of Local Minima in Manifold Learning
  1093. 4.33, 1.25, [3, 4, 6] [5, 3, 3]On the Convergence and Robustness of Batch Normalization
  1094. 4.33, 0.47, [4, 5, 4] [4, 5, 4]Sample Efficient Deep Neuroevolution in Low Dimensional Latent Space
  1095. 4.33, 1.25, [6, 3, 4] [3, 5, 4]Context-adaptive Entropy Model for End-to-end Optimized Image Compression
  1096. 4.33, 1.25, [4, 3, 6] [4, 4, 3]An Adversarial Learning Framework for a Persona-based Multi-turn Dialogue Model
  1097. 4.33, 0.47, [4, 4, 5] [4, 4, 4]ODIN: Outlier Detection In Neural Networks
  1098. 4.33, 0.47, [5, 4, 4] [4, 4, 4]Log Hyperbolic Cosine Loss Improves Variational Auto-Encoder
  1099. 4.33, 0.94, [5, 3, 5] [4, 4, 4]Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
  1100. 4.33, 0.47, [5, 4, 4] [3, 5, 3]A preconditioned accelerated stochastic gradient descent algorithm
  1101. 4.33, 0.47, [4, 4, 5] [4, 3, 3]Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN
  1102. 4.33, 0.47, [5, 4, 4] [3, 4, 4]Efficient Convolutional Neural Network Training with Direct Feedback Alignment
  1103. 4.33, 1.25, [3, 4, 6] [5, 5, 2]LEARNING ADVERSARIAL EXAMPLES WITH RIEMANNIAN GEOMETRY
  1104. 4.33, 0.47, [4, 5, 4] [5, 3, 5]SHAMANN: Shared Memory Augmented Neural Networks
  1105. 4.33, 0.47, [4, 4, 5] [4, 3, 3]Adaptive Convolutional Neural Networks
  1106. 4.33, 1.25, [3, 6, 4] [4, 3, 3]Pixel Redrawn For A Robust Adversarial Defense
  1107. 4.33, 0.47, [4, 4, 5] [4, 3, 3]DeepTwist: Learning Model Compression via Occasional Weight Distortion
  1108. 4.33, 1.25, [4, 6, 3] [3, 3, 5]Wasserstein proximal of GANs
  1109. 4.33, 1.25, [4, 3, 6] [4, 4, 2]Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
  1110. 4.33, 0.94, [3, 5, 5] [5, 2, 3]Exploration by Uncertainty in Reward Space
  1111. 4.33, 0.47, [4, 5, 4] [4, 4, 5]Contextualized Role Interaction for Neural Machine Translation
  1112. 4.33, 0.47, [4, 4, 5] [4, 4, 3]Escaping Flat Areas via Function-Preserving Structural Network Modifications
  1113. 4.33, 0.47, [4, 5, 4] [4, 3, 4]DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
  1114. 4.33, 0.47, [4, 5, 4] [4, 4, 3]Classifier-agnostic saliency map extraction
  1115. 4.33, 0.47, [4, 5, 4] [4, 4, 3]PRUNING WITH HINTS: AN EFFICIENT FRAMEWORK FOR MODEL ACCELERATION
  1116. 4.33, 0.94, [3, 5, 5] [3, 4, 4]Meta-Learning with Individualized Feature Space for Few-Shot Classification
  1117. 4.33, 0.94, [5, 5, 3] [2, 2, 3]Downsampling leads to Image Memorization in Convolutional Autoencoders
  1118. 4.33, 1.25, [3, 6, 4] [4, 5, 3]FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS
  1119. 4.33, 0.47, [4, 5, 4] [4, 3, 4]Generative Models from the perspective of Continual Learning
  1120. 4.33, 0.47, [4, 5, 4] [3, 5, 5]Total Style Transfer with a Single Feed-Forward Network
  1121. 4.33, 0.94, [3, 5, 5] [4, 5, 5]A fast quasi-Newton-type method for large-scale stochastic optimisation
  1122. 4.33, 0.94, [5, 5, 3] [4, 4, 4]Explainable Adversarial Learning: Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness
  1123. 4.33, 0.47, [5, 4, 4] [4, 5, 5]Universal Attacks on Equivariant Networks
  1124. 4.33, 0.94, [5, 5, 3] [4, 4, 4]Compound Density Networks
  1125. 4.33, 0.47, [4, 5, 4] [5, 2, 3]A Guider Network for Multi-Dual Learning
  1126. 4.33, 0.94, [5, 5, 3] [3, 4, 4]ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS
  1127. 4.33, 0.47, [4, 5, 4] [4, 3, 4]Recovering the Lowest Layer of Deep Networks with High Threshold Activations
  1128. 4.33, 2.05, [2, 4, 7] [5, 3, 3]Mental Fatigue Monitoring using Brain Dynamics Preferences
  1129. 4.33, 0.47, [4, 4, 5] [4, 4, 3]Progressive Weight Pruning Of Deep Neural Networks Using ADMM
  1130. 4.33, 0.47, [4, 4, 5] [3, 4, 4]MixFeat: Mix Feature in Latent Space Learns Discriminative Space
  1131. 4.33, 0.47, [4, 4, 5] [4, 4, 3]The Cakewalk Method
  1132. 4.33, 1.25, [3, 6, 4] [4, 4, 4]On Generalization Bounds of a Family of Recurrent Neural Networks
  1133. 4.33, 1.25, [6, 4, 3] [3, 4, 4]Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection
  1134. 4.33, 0.47, [4, 4, 5] [4, 4, 4]In Your Pace: Learning the Right Example at the Right Time
  1135. 4.33, 0.94, [5, 3, 5] [3, 3, 3]Backdrop: Stochastic Backpropagation
  1136. 4.33, 0.47, [5, 4, 4] [3, 5, 4]SENSE: SEMANTICALLY ENHANCED NODE SEQUENCE EMBEDDING
  1137. 4.33, 0.47, [4, 5, 4] [5, 4, 5]Task-GAN for Improved GAN based Image Restoration
  1138. 4.33, 0.47, [4, 4, 5] [5, 3, 4]EFFICIENT SEQUENCE LABELING WITH ACTOR-CRITIC TRAINING
  1139. 4.33, 1.25, [4, 6, 3] [4, 4, 4]Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks
  1140. 4.33, 0.47, [4, 4, 5] [4, 4, 3]Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-valued Inverse Reinforcement Learning
  1141. 4.33, 0.47, [5, 4, 4] [3, 5, 3]Combining Learned Representations for Combinatorial Optimization
  1142. 4.33, 0.47, [4, 4, 5] [4, 4, 4]From Nodes to Networks: Evolving Recurrent Neural Networks
  1143. 4.33, 0.94, [5, 5, 3] [3, 4, 5]DppNet: Approximating Determinantal Point Processes with Deep Networks
  1144. 4.33, 0.47, [5, 4, 4] [4, 4, 4]Implicit Maximum Likelihood Estimation
  1145. 4.33, 0.47, [5, 4, 4] [4, 4, 4]Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles
  1146. 4.33, 0.47, [4, 4, 5] [5, 4, 4]Asynchronous SGD without gradient delay for efficient distributed training
  1147. 4.33, 0.47, [4, 5, 4] [3, 3, 3]On the effect of the activation function on the distribution of hidden nodes in a deep network
  1148. 4.33, 1.25, [3, 4, 6] [5, 4, 4]Learning Corresponded Rationales for Text Matching
  1149. 4.33, 1.25, [4, 3, 6] [3, 3, 3]REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS
  1150. 4.33, 1.89, [3, 3, 7] [5, 4, 4]Meta-Learning to Guide Segmentation
  1151. 4.33, 1.89, [7, 3, 3] [4, 4, 5]Recycling the discriminator for improving the inference mapping of GAN
  1152. 4.33, 0.47, [5, 4, 4] [4, 4, 5]A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning
  1153. 4.33, 1.25, [4, 6, 3] [4, 4, 4]Neural Probabilistic Motor Primitives for Humanoid Control
  1154. 4.33, 1.70, [5, 2, 6] [3, 4, 3]Dual Learning: Theoretical Study and Algorithmic Extensions
  1155. 4.33, 0.47, [5, 4, 4] [4, 3, 4]Visual Imitation Learning with Recurrent Siamese Networks
  1156. 4.33, 0.47, [4, 5, 4] [5, 3, 3]Learning Hash Codes via Hamming Distance Targets
  1157. 4.33, 0.94, [3, 5, 5] [5, 4, 3]Improving Sample-based Evaluation for Generative Adversarial Networks
  1158. 4.33, 1.25, [6, 3, 4] [4, 5, 1]Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes
  1159. 4.33, 0.47, [4, 5, 4] [5, 4, 3]Successor Uncertainties: exploration and uncertainty in temporal difference learning
  1160. 4.33, 0.47, [4, 4, 5] [5, 3, 4]Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units
  1161. 4.33, 0.47, [4, 4, 5] [5, 4, 4]Pseudosaccades: A simple ensemble scheme for improving classification performance of deep nets
  1162. 4.33, 1.25, [3, 4, 6] [5, 5, 2]Modeling Dynamics of Biological Systems with Deep Generative Neural Networks
  1163. 4.33, 0.47, [5, 4, 4] [5, 4, 5]A SINGLE SHOT PCA-DRIVEN ANALYSIS OF NETWORK STRUCTURE TO REMOVE REDUNDANCY
  1164. 4.33, 0.47, [5, 4, 4] [4, 4, 4]Over-parameterization Improves Generalization in the XOR Detection Problem
  1165. 4.33, 0.47, [4, 4, 5] [4, 4, 5]Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data
  1166. 4.33, 0.47, [4, 4, 5] [4, 3, 4]Rating Continuous Actions in Spatial Multi-Agent Problems
  1167. 4.33, 0.47, [4, 5, 4] [3, 3, 4]Adversarial Examples Are a Natural Consequence of Test Error in Noise
  1168. 4.33, 1.25, [4, 3, 6] [4, 5, 4]Where and when to look? Spatial-temporal attention for action recognition in videos
  1169. 4.33, 0.47, [4, 5, 4] [4, 4, 5]LARGE BATCH SIZE TRAINING OF NEURAL NETWORKS WITH ADVERSARIAL TRAINING AND SECOND-ORDER INFORMATION
  1170. 4.33, 1.25, [6, 3, 4] [5, 4, 1]Teaching to Teach by Structured Dark Knowledge
  1171. 4.33, 0.94, [5, 5, 3] [4, 4, 3]Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility
  1172. 4.33, 0.47, [4, 4, 5] [4, 5, 4]End-to-End Hierarchical Text Classification with Label Assignment Policy
  1173. 4.33, 1.89, [3, 3, 7] [4, 4, 3]Structured Prediction using cGANs with Fusion Discriminator
  1174. 4.33, 1.25, [6, 4, 3] [5, 4, 4]Open Vocabulary Learning on Source Code with a Graph-Structured Cache
  1175. 4.33, 0.94, [3, 5, 5] [4, 3, 3]Modulated Variational Auto-Encoders for Many-to-Many Musical Timbre Transfer
  1176. 4.33, 0.94, [5, 3, 5] [3, 5, 3]Variational recurrent models for representation learning
  1177. 4.33, 0.94, [5, 3, 5] [4, 5, 4]Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video
  1178. 4.33, 0.47, [4, 4, 5] [4, 4, 4]Do Language Models Have Common Sense?
  1179. 4.33, 0.94, [5, 5, 3] [3, 4, 5]Model-Agnostic Meta-Learning for Multimodal Task Distributions
  1180. 4.33, 0.47, [4, 5, 4] [4, 3, 4]How Training Data Affect the Accuracy and Robustness of Neural Networks for Image Classification
  1181. 4.33, 1.25, [3, 6, 4] [5, 2, 5]Locally Linear Unsupervised Feature Selection
  1182. 4.33, 0.47, [5, 4, 4] [4, 4, 3]SALSA-TEXT : SELF ATTENTIVE LATENT SPACE BASED ADVERSARIAL TEXT GENERATION
  1183. 4.33, 0.47, [4, 5, 4] [5, 5, 5]Harmonic Unpaired Image-to-image Translation
  1184. 4.33, 1.25, [6, 3, 4] [3, 4, 4]On Meaning-Preserving Adversarial Perturbations for Sequence-to-Sequence Models
  1185. 4.33, 0.94, [5, 5, 3] [3, 4, 1]Meta-Learning Neural Bloom Filters
  1186. 4.33, 0.47, [4, 4, 5] [4, 4, 3]BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks
  1187. 4.33, 1.25, [3, 6, 4] [5, 4, 4]Optimal Attacks against Multiple Classifiers
  1188. 4.33, 0.47, [4, 4, 5] [4, 4, 2]Evolutionary-Neural Hybrid Agents for Architecture Search
  1189. 4.33, 1.89, [7, 3, 3] [2, 4, 4]On Inductive Biases in Deep Reinforcement Learning
  1190. 4.33, 1.25, [4, 3, 6] [3, 4, 3]W2GAN: RECOVERING AN OPTIMAL TRANSPORTMAP WITH A GAN
  1191. 4.33, 0.94, [5, 5, 3] [2, 4, 4]Latent Transformations for Object View Points Synthesis
  1192. 4.33, 1.25, [4, 3, 6] [3, 5, 4]Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
  1193. 4.33, 0.47, [4, 5, 4] [3, 3, 3]Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
  1194. 4.33, 0.94, [3, 5, 5] [4, 2, 4]Multi-Objective Value Iteration with Parameterized Threshold-Based Safety Constraints
  1195. 4.33, 0.47, [5, 4, 4] [4, 2, 4]Select Via Proxy: Efficient Data Selection For Training Deep Networks
  1196. 4.33, 0.47, [5, 4, 4] [3, 5, 3]Variational Domain Adaptation
  1197. 4.33, 0.47, [4, 5, 4] [4, 5, 5]COMPOSITION AND DECOMPOSITION OF GANS
  1198. 4.33, 0.94, [5, 5, 3] [4, 5, 4]PIE: Pseudo-Invertible Encoder
  1199. 4.33, 0.47, [5, 4, 4] [4, 4, 2]TopicGAN: Unsupervised Text Generation from Explainable Latent Topics
  1200. 4.33, 0.47, [4, 5, 4] [3, 3, 4]NICE: noise injection and clamping estimation for neural network quantization
  1201. 4.33, 0.94, [5, 3, 5] [3, 5, 5]Network Reparameterization for Unseen Class Categorization
  1202. 4.33, 0.94, [3, 5, 5] [4, 3, 3]Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning
  1203. 4.33, 1.25, [4, 3, 6] [4, 3, 4]Architecture Compression
  1204. 4.33, 1.89, [3, 7, 3] [3, 3, 2]A model cortical network for spatiotemporal sequence learning and prediction
  1205. 4.33, 0.47, [4, 4, 5] [4, 3, 2]Modulating transfer between tasks in gradient-based meta-learning
  1206. 4.33, 0.47, [4, 4, 5] [3, 4, 3]Mean Replacement Pruning
  1207. 4.33, 0.47, [4, 5, 4] [4, 5, 3]Stochastic Quantized Activation: To prevent Overfitting in Fast Adversarial Training
  1208. 4.33, 0.94, [5, 3, 5] [3, 4, 3]Provable Defenses against Spatially Transformed Adversarial Inputs: Impossibility and Possibility Results
  1209. 4.33, 0.94, [5, 3, 5] [5, 3, 4]Learning Physics Priors for Deep Reinforcement Learing
  1210. 4.33, 1.25, [3, 4, 6] [5, 4, 3]Looking inside the black box: assessing the modular structure of deep generative models with counterfactuals
  1211. 4.33, 0.47, [5, 4, 4] [4, 4, 5]Correction Networks: Meta-Learning for Zero-Shot Learning
  1212. 4.33, 0.94, [5, 5, 3] [2, 3, 5]Assessing Generalization in Deep Reinforcement Learning
  1213. 4.33, 0.94, [5, 3, 5] [4, 3, 4]Bridging HMMs and RNNs through Architectural Transformations
  1214. 4.33, 0.47, [4, 4, 5] [4, 2, 4]Variadic Learning by Bayesian Nonparametric Deep Embedding
  1215. 4.25, 0.43, [5, 4, 4, 4] [3, 4, 5, 2]Characterizing the Accuracy/Complexity Landscape of Explanations of Deep Networks through Knowledge Extraction
  1216. 4.25, 0.43, [5, 4, 4, 4] [3, 4, 3, 3]A Priori Estimates of the Generalization Error for Two-layer Neural Networks
  1217. 4.25, 0.43, [5, 4, 4, 4] [3, 4, 4, 5]Countdown Regression: Sharp and Calibrated Survival Predictions
  1218. 4.25, 1.48, [2, 4, 6, 5] [4, 3, 4, 3]Understanding the Asymptotic Performance of Model-Based RL Methods
  1219. 4.25, 0.43, [4, 5, 4, 4] [4, 4, 3, 4]Unlabeled Disentangling of GANs with Guided Siamese Networks
  1220. 4.25, 0.43, [4, 4, 4, 5] [4, 5, 5, 4]Discovering General-Purpose Active Learning Strategies
  1221. 4.00, 0.00, [4, 4, 4] [5, 4, 4]Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourages convex latent distributions
  1222. 4.00, 0.82, [4, 3, 5] [4, 5, 5]The Forward-Backward Embedding of Directed Graphs
  1223. 4.00, 1.41, [6, 3, 3] [4, 5, 3]Large-scale classification of structured objects using a CRF with deep class embedding
  1224. 4.00, 0.00, [4, 4, 4] [5, 4, 4]Overcoming catastrophic forgetting through weight consolidation and long-term memory
  1225. 4.00, 0.82, [4, 3, 5] [4, 4, 5]Neural Network Cost Landscapes as Quantum States
  1226. 4.00, 0.82, [5, 3, 4] [4, 4, 5]Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
  1227. 4.00, 0.82, [4, 3, 5] [3, 5, 2]Learning Latent Semantic Representation from Pre-defined Generative Model
  1228. 4.00, 0.00, [4, 4, 4] [5, 4, 3]HC-Net: Memory-based Incremental Dual-Network System for Continual learning
  1229. 4.00, 0.00, [4, 4, 4] [4, 4, 5]Sequence Modelling with Memory-Augmented Recurrent Neural Networks
  1230. 4.00, 0.82, [3, 5, 4] [4, 3, 4]MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR
  1231. 4.00, 0.00, [4, 4] [1, 2]S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks
  1232. 4.00, 0.82, [3, 4, 5] [4, 3, 4]Difference-Seeking Generative Adversarial Network
  1233. 4.00, 0.82, [5, 4, 3] [4, 4, 4]Semantic Parsing via Cross-Domain Schema
  1234. 4.00, 0.82, [5, 4, 3] [4, 4, 5]On the Selection of Initialization and Activation Function for Deep Neural Networks
  1235. 4.00, 0.00, [4, 4, 4] [3, 4, 3]Deep processing of structured data
  1236. 4.00, 0.82, [3, 5, 4] [4, 4, 4]Better Accuracy with Quantified Privacy: Representations Learned via Reconstructive Adversarial Network
  1237. 4.00, 0.82, [5, 4, 3] [3, 4, 3]Modular Deep Probabilistic Programming
  1238. 4.00, 0.00, [4, 4, 4] [3, 5, 4]Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning
  1239. 4.00, 0.82, [5, 4, 3] [4, 5, 4]A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing
  1240. 4.00, 0.82, [4, 3, 5] [4, 4, 4]Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
  1241. 4.00, 0.82, [5, 3, 4] [4, 4, 4]Assumption Questioning: Latent Copying and Reward Exploitation in Question Generation
  1242. 4.00, 0.82, [4, 3, 5] [5, 4, 3]Polar Prototype Networks
  1243. 4.00, 0.82, [3, 5, 4] [4, 4, 5]Applications of Gaussian Processes in Finance
  1244. 4.00, 0.82, [5, 4, 3] [4, 4, 4]Incremental Hierarchical Reinforcement Learning with Multitask LMDPs
  1245. 4.00, 0.82, [3, 4, 5] [3, 4, 3]On the Statistical and Information Theoretical Characteristics of DNN Representations
  1246. 4.00, 0.00, [4, 4, 4] [4, 5, 4]Explaining Neural Networks Semantically and Quantitatively
  1247. 4.00, 1.41, [6, 3, 3] [3, 3, 3]microGAN: Promoting Variety through Microbatch Discrimination
  1248. 4.00, 0.82, [5, 3, 4] [4, 5, 2]PA-GAN: Improving GAN Training by Progressive Augmentation
  1249. 4.00, 0.00, [4, 4, 4] [4, 3, 2]Deep Generative Models for learning Coherent Latent Representations from Multi-Modal Data
  1250. 4.00, 0.82, [3, 5, 4] [4, 3, 4]Overfitting Detection of Deep Neural Networks without a Hold Out Set
  1251. 4.00, 0.00, [4, 4, 4] [3, 4, 5]Mol-CycleGAN – a generative model for molecular optimization
  1252. 4.00, 0.00, [4, 4, 4] [4, 4, 4]NUTS: Network for Unsupervised Telegraphic Summarization
  1253. 4.00, 0.00, [4, 4, 4] [4, 4, 3]Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning
  1254. 4.00, 0.82, [4, 5, 3] [4, 3, 4]Few-shot Classification on Graphs with Structural Regularized GCNs
  1255. 4.00, 0.82, [3, 5, 4] [5, 3, 5]Second-Order Adversarial Attack and Certifiable Robustness
  1256. 4.00, 1.63, [4, 2, 6] [3, 5, 2]Reinforcement Learning: From temporal to spatial value decomposition
  1257. 4.00, 0.00, [4, 4, 4] [4, 4, 5]EXPLORATION OF EFFICIENT ON-DEVICE ACOUSTIC MODELING WITH NEURAL NETWORKS
  1258. 4.00, 1.41, [5, 2, 5] [5, 4, 4]The effectiveness of layer-by-layer training using the information bottleneck principle
  1259. 4.00, 0.82, [3, 5, 4] [4, 3, 3]Layerwise Recurrent Autoencoder for General Real-world Traffic Flow Forecasting
  1260. 4.00, 0.82, [3, 4, 5] [4, 4, 5]ON THE USE OF CONVOLUTIONAL AUTO-ENCODER FOR INCREMENTAL CLASSIFIER LEARNING IN CONTEXT AWARE ADVERTISEMENT
  1261. 4.00, 0.00, [4, 4, 4] [4, 4, 4]ChainGAN: A sequential approach to GANs
  1262. 4.00, 0.00, [4, 4, 4] [4, 5, 5]Activity Regularization for Continual Learning
  1263. 4.00, 0.82, [5, 4, 3] [3, 4, 5]Robustness and Equivariance of Neural Networks
  1264. 4.00, 0.82, [5, 4, 3] [4, 4, 3]Distributionally Robust Optimization Leads to Better Generalization: on SGD and Beyond
  1265. 4.00, 0.82, [5, 3, 4] [4, 4, 4]D2KE: From Distance to Kernel and Embedding via Random Features For Structured Inputs
  1266. 4.00, 0.00, [4, 4, 4] [4, 4, 5]Hyper-Regularization: An Adaptive Choice for the Learning Rate in Gradient Descent
  1267. 4.00, 0.82, [4, 5, 3] [3, 5, 5]Complexity of Training ReLU Neural Networks
  1268. 4.00, 1.41, [2, 4, 4, 6] [5, 4, 2, 2]Efficient Exploration through Bayesian Deep Q-Networks
  1269. 4.00, 0.82, [4, 5, 3] [4, 5, 4]Sequenced-Replacement Sampling for Deep Learning
  1270. 4.00, 0.00, [4, 4, 4] [4, 5, 5]DEEP ADVERSARIAL FORWARD MODEL
  1271. 4.00, 0.82, [5, 4, 3] [3, 4, 4]Look Ma, No GANs! Image Transformation with ModifAE
  1272. 4.00, 0.82, [4, 5, 3] [3, 3, 4]Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
  1273. 4.00, 0.82, [5, 4, 3] [4, 4, 5]ACIQ: Analytical Clipping for Integer Quantization of neural networks
  1274. 4.00, 0.82, [5, 4, 3] [4, 3, 4]Constrained Bayesian Optimization for Automatic Chemical Design
  1275. 4.00, 0.82, [3, 5, 4] [4, 3, 5]The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions
  1276. 4.00, 0.00, [4, 4, 4] [4, 3, 4]Co-manifold learning with missing data
  1277. 4.00, 0.00, [4, 4] [5, 4]Fast Binary Functional Search on Graph
  1278. 4.00, 0.82, [3, 4, 5] [4, 3, 4]Towards More Theoretically-Grounded Particle Optimization Sampling for Deep Learning
  1279. 4.00, 0.00, [4, 4, 4] [4, 3, 4]Differentially Private Federated Learning: A Client Level Perspective
  1280. 4.00, 0.82, [3, 5, 4] [4, 2, 4]UaiNets: From Unsupervised to Active Deep Anomaly Detection
  1281. 4.00, 0.82, [5, 4, 3] [4, 4, 4]Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy
  1282. 4.00, 0.82, [5, 3, 4] [3, 4, 3]In search of theoretically grounded pruning
  1283. 4.00, 1.63, [2, 4, 6] [5, 3, 5]Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?
  1284. 4.00, 0.82, [3, 4, 5] [5, 4, 4]Learning Representations in Model-Free Hierarchical Reinforcement Learning
  1285. 4.00, 0.82, [5, 3, 4] [4, 5, 4]Dual Importance Weight GAN
  1286. 4.00, 0.00, [4, 4, 4] [5, 5, 4]Relational Graph Attention Networks
  1287. 4.00, 0.00, [4, 4, 4] [3, 4, 5]HyperGAN: Exploring the Manifold of Neural Networks
  1288. 4.00, 0.82, [4, 3, 5] [5, 5, 3]Generalized Capsule Networks with Trainable Routing Procedure
  1289. 4.00, 0.82, [4, 3, 5] [2, 2, 4]Distilled Agent DQN for Provable Adversarial Robustness
  1290. 4.00, 0.00, [4, 4, 4] [4, 5, 4]Distinguishability of Adversarial Examples
  1291. 4.00, 1.41, [6, 3, 3] [4, 5, 4]Iteratively Learning from the Best
  1292. 4.00, 0.82, [5, 3, 4] [3, 4, 3]Evaluating GANs via Duality
  1293. 4.00, 0.82, [4, 3, 5] [3, 4, 4]Constraining Action Sequences with Formal Languages for Deep Reinforcement Learning
  1294. 4.00, 0.82, [4, 3, 5] [5, 5, 4]Overlapping Community Detection with Graph Neural Networks
  1295. 4.00, 0.82, [4, 5, 3] [4, 3, 5]DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
  1296. 4.00, 0.00, [4, 4, 4] [5, 4, 4]Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding
  1297. 4.00, 0.82, [4, 5, 3] [4, 3, 3]Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring
  1298. 4.00, 2.16, [5, 1, 6] [4, 4, 4]Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets
  1299. 4.00, 0.82, [4, 3, 5] [4, 3, 4]Reconciling Feature-Reuse and Overfitting in DenseNet with Specialized Dropout
  1300. 4.00, 0.82, [3, 5, 4] [4, 5, 4]N/A
  1301. 4.00, 0.82, [5, 4, 3] [5, 4, 5]Training Hard-Threshold Networks with Combinatorial Search in a Discrete Target Propagation Setting
  1302. 4.00, 0.00, [4, 4, 4] [4, 4, 4]Latent Domain Transfer: Crossing modalities with Bridging Autoencoders
  1303. 4.00, 0.82, [4, 3, 5] [5, 4, 3]Neural Regression Tree
  1304. 4.00, 0.82, [5, 3, 4] [5, 2, 4]Neural MMO: A massively multiplayer game environment for intelligent agents
  1305. 4.00, 0.00, [4, 4, 4] [4, 4, 4]Uncertainty-guided Lifelong Learning in Bayesian Networks
  1306. 4.00, 0.00, [4, 4, 4] [5, 3, 5]Language Modeling with Graph Temporal Convolutional Networks
  1307. 4.00, 0.82, [4, 5, 3] [4, 5, 5]RNNs with Private and Shared Representations for Semi-Supervised Sequence Learning
  1308. 4.00, 1.41, [2, 5, 5] [2, 2, 3]Universal discriminative quantum neural networks
  1309. 4.00, 0.00, [4, 4, 4] [4, 4, 5]Learning to Search Efficient DenseNet with Layer-wise Pruning
  1310. 4.00, 0.82, [3, 5, 4] [5, 4, 5]Understanding Opportunities for Efficiency in Single-image Super Resolution Networks
  1311. 4.00, 0.82, [3, 4, 5] [3, 5, 4]Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning
  1312. 4.00, 0.82, [3, 5, 4] [5, 4, 4]Deepström Networks
  1313. 4.00, 0.82, [4, 3, 5] [4, 3, 4]Pearl: Prototype lEArning via Rule Lists
  1314. 4.00, 0.82, [3, 5, 4] [4, 2, 5]Reinforced Pipeline Optimization: Behaving Optimally with Non-Differentiabilities
  1315. 4.00, 0.00, [4, 4, 4] [4, 4, 4]Trajectory VAE for multi-modal imitation
  1316. 4.00, 0.00, [4, 4, 4] [3, 4, 5]DATA POISONING ATTACK AGAINST NODE EMBEDDING METHODS
  1317. 4.00, 0.00, [4, 4, 4] [4, 3, 4]Unsupervised Exploration with Deep Model-Based Reinforcement Learning
  1318. 4.00, 1.63, [2, 6, 4] [4, 4, 3]On the Trajectory of Stochastic Gradient Descent in the Information Plane
  1319. 4.00, 0.82, [5, 4, 3] [2, 4, 3]Functional Bayesian Neural Networks for Model Uncertainty Quantification
  1320. 4.00, 1.63, [6, 2, 4] [4, 4, 4]REVISTING NEGATIVE TRANSFER USING ADVERSARIAL LEARNING
  1321. 4.00, 0.00, [4, 4, 4] [3, 4, 4]Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
  1322. 4.00, 0.00, [4, 4, 4] [5, 3, 4]Ain’t Nobody Got Time for Coding: Structure-Aware Program Synthesis from Natural Language
  1323. 4.00, 0.00, [4, 4, 4] [4, 3, 4]Graph Generation via Scattering
  1324. 4.00, 1.41, [3, 3, 6] [2, 5, 4]Improving machine classification using human uncertainty measurements
  1325. 4.00, 0.82, [3, 4, 5] [4, 5, 3]Empirically Characterizing Overparameterization Impact on Convergence
  1326. 4.00, 0.00, [4, 4, 4] [4, 5, 4]Continual Learning via Explicit Structure Learning
  1327. 3.67, 1.25, [5, 2, 4] [5, 4, 4]R ESIDUAL NETWORKS CLASSIFY INPUTS BASED ON THEIR NEURAL TRANSIENT DYNAMICS
  1328. 3.67, 0.47, [4, 3, 4] [5, 3, 4]Diminishing Batch Normalization
  1329. 3.67, 1.70, [6, 3, 2] [3, 4, 1]Filter Training and Maximum Response: Classification via Discerning
  1330. 3.67, 1.25, [4, 2, 5] [4, 4, 5]Optimizing for Generalization in Machine Learning with Cross-Validation Gradients
  1331. 3.67, 0.47, [3, 4, 4] [3, 4, 3]Image Score: how to select useful samples
  1332. 3.67, 0.47, [3, 4, 4] [3, 4, 2]Feature Attribution As Feature Selection
  1333. 3.67, 1.25, [5, 4, 2] [3, 5, 5]Discrete Structural Planning for Generating Diverse Translations
  1334. 3.67, 0.47, [4, 4, 3] [3, 4, 4]DynCNN: An Effective Dynamic Architecture on Convolutional Neural Network for Surveillance Videos
  1335. 3.67, 0.47, [4, 3, 4] [4, 5, 3]An Attention-Based Model for Learning Dynamic Interaction Networks
  1336. 3.67, 2.49, [3, 1, 7] [4, 5, 3]Optimization on Multiple Manifolds
  1337. 3.67, 0.47, [3, 4, 4] [4, 5, 4]RETHINKING SELF-DRIVING : MULTI -TASK KNOWLEDGE FOR BETTER GENERALIZATION AND ACCIDENT EXPLANATION ABILITY
  1338. 3.67, 2.49, [1, 7, 3] [5, 3, 4]Why Do Neural Response Generation Models Prefer Universal Replies?
  1339. 3.67, 0.47, [4, 3, 4] [4, 4, 4]DelibGAN: Coarse-to-Fine Text Generation via Adversarial Network
  1340. 3.67, 0.47, [3, 4, 4] [4, 5, 4]Encoding Category Trees Into Word-Embeddings Using Geometric Approach
  1341. 3.67, 0.94, [3, 3, 5] [5, 5, 4]GradMix: Multi-source Transfer across Domains and Tasks
  1342. 3.67, 0.47, [3, 4, 4] [5, 4, 3]Synthnet: Learning synthesizers end-to-end
  1343. 3.67, 0.47, [4, 4, 3] [5, 4, 4]Prior Networks for Detection of Adversarial Attacks
  1344. 3.67, 0.94, [3, 3, 5] [5, 4, 3]Localized random projections challenge benchmarks for bio-plausible deep learning
  1345. 3.67, 0.94, [3, 5, 3] [2, 2, 3]A fully automated periodicity detection in time series
  1346. 3.67, 0.47, [4, 4, 3] [5, 4, 4]Generating Images from Sounds Using Multimodal Features and GANs
  1347. 3.67, 0.94, [5, 3, 3] [4, 5, 4]Text Embeddings for Retrieval from a Large Knowledge Base
  1348. 3.67, 0.47, [4, 4, 3] [5, 4, 5]Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
  1349. 3.67, 0.47, [3, 4, 4] [3, 4, 4]Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks
  1350. 3.67, 0.47, [4, 4, 3] [4, 3, 4]Learning agents with prioritization and parameter noise in continuous state and action space
  1351. 3.67, 0.47, [4, 3, 4] [3, 5, 4]Hierarchical Attention: What Really Counts in Various NLP Tasks
  1352. 3.67, 0.47, [3, 4, 4] [4, 3, 4]Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks
  1353. 3.67, 0.47, [4, 3, 4] [4, 2, 4]Using Deep Siamese Neural Networks to Speed up Natural Products Research
  1354. 3.67, 0.47, [4, 3, 4] [3, 5, 4]Graph Spectral Regularization For Neural Network Interpretability
  1355. 3.67, 0.47, [4, 4, 3] [4, 3, 5]Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning
  1356. 3.67, 0.47, [4, 4, 3] [2, 4, 4]Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks
  1357. 3.67, 0.47, [4, 3, 4] [5, 5, 4]Question Generation using a Scratchpad Encoder
  1358. 3.67, 0.47, [3, 4, 4] [4, 4, 4]Adversarially Robust Training through Structured Gradient Regularization
  1359. 3.67, 0.47, [3, 4, 4] [5, 4, 3]GEOMETRIC AUGMENTATION FOR ROBUST NEURAL NETWORK CLASSIFIERS
  1360. 3.67, 1.25, [2, 5, 4] [4, 3, 4]DEEP HIERARCHICAL MODEL FOR HIERARCHICAL SELECTIVE CLASSIFICATION AND ZERO SHOT LEARNING
  1361. 3.67, 0.47, [4, 4, 3] [4, 5, 4]Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
  1362. 3.67, 0.47, [4, 3, 4] [5, 4, 3]Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning
  1363. 3.67, 0.47, [3, 4, 4] [4, 4, 5]Normalization Gradients are Least-squares Residuals
  1364. 3.67, 0.47, [4, 3, 4] [5, 4, 4]DEEP GEOMETRICAL GRAPH Classification WITH DYNAMIC POOLING
  1365. 3.67, 1.25, [4, 2, 5] [4, 5, 4]Differentiable Greedy Networks
  1366. 3.67, 1.25, [5, 2, 4] [4, 5, 4]Kmer2vec: Towards transcriptomic representations by learning kmer embeddings
  1367. 3.67, 0.47, [4, 3, 4] [4, 4, 5]Graph Learning Network: A Structure Learning Algorithm
  1368. 3.67, 0.47, [3, 4, 4] [3, 5, 4]Controlling Over-generalization and its Effect on Adversarial Examples Detection and Generation
  1369. 3.67, 0.47, [4, 3, 4] [4, 4, 4]PCNN: Environment Adaptive Model Without Finetuning
  1370. 3.67, 0.47, [3, 4, 4] [4, 4, 5]Optimized Gated Deep Learning Architectures for Sensor Fusion
  1371. 3.67, 0.47, [3, 4, 4] [4, 3, 5]A Walk with SGD: How SGD Explores Regions of Deep Network Loss?
  1372. 3.67, 0.94, [3, 3, 5] [4, 4, 3]Automatic generation of object shapes with desired functionalities
  1373. 3.67, 0.47, [4, 4, 3] [4, 5, 5]Dynamic Recurrent Language Model
  1374. 3.67, 0.94, [3, 5, 3] [5, 1, 4]D-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
  1375. 3.67, 0.47, [3, 4, 4] [4, 3, 4]Inhibited Softmax for Uncertainty Estimation in Neural Networks
  1376. 3.67, 0.47, [4, 4, 3] [5, 5, 4]Unsupervised Video-to-Video Translation
  1377. 3.67, 0.47, [3, 4, 4] [4, 3, 4]Efficient Federated Learning via Variational Dropout
  1378. 3.67, 0.47, [4, 3, 4] [5, 5, 4]Contextual Recurrent Convolutional Model for Robust Visual Learning
  1379. 3.67, 0.47, [4, 4, 3] [4, 4, 4]Unsupervised one-to-many image translation
  1380. 3.67, 0.47, [3, 4, 4] [4, 3, 4]INTERPRETABLE CONVOLUTIONAL FILTER PRUNING
  1381. 3.67, 0.94, [3, 3, 5] [5, 4, 3]Fake Sentence Detection as a Training Task for Sentence Encoding
  1382. 3.67, 0.47, [4, 4, 3] [5, 3, 3]Accelerating first order optimization algorithms
  1383. 3.67, 0.94, [3, 3, 5] [4, 4, 4]The Natural Language Decathlon: Multitask Learning as Question Answering
  1384. 3.50, 1.12, [5, 2, 3, 4] [2, 5, 2, 3]Learning to Reinforcement Learn by Imitation
  1385. 3.50, 0.50, [3, 3, 4, 4] [4, 2, 4, 3]LSH Microbatches for Stochastic Gradients: Value in Rearrangement
  1386. 3.33, 0.47, [4, 3, 3] [3, 4, 4]Linearizing Visual Processes with Deep Generative Models
  1387. 3.33, 0.47, [3, 3, 4] [4, 4, 3]Interpreting Layered Neural Networks via Hierarchical Modular Representation
  1388. 3.33, 0.94, [4, 2, 4] [5, 4, 3]IEA: Inner Ensemble Average within a convolutional neural network
  1389. 3.33, 0.47, [3, 3, 4] [4, 4, 4]Accidental exploration through value predictors
  1390. 3.33, 0.47, [3, 3, 4] [5, 4, 3]Learning and Data Selection in Big Datasets
  1391. 3.33, 0.47, [3, 3, 4] [4, 5, 4]Human Action Recognition Based on Spatial-Temporal Attention
  1392. 3.33, 0.47, [3, 3, 4] [5, 3, 4]SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization
  1393. 3.33, 0.47, [3, 4, 3] [5, 4, 4]Encoder Discriminator Networks for Unsupervised Representation Learning
  1394. 3.33, 0.47, [4, 3, 3] [4, 4, 5]Understanding and Improving Sequence-Labeling NER with Self-Attentive LSTMs
  1395. 3.33, 1.25, [3, 5, 2] [4, 5, 5]Geometric Operator Convolutional Neural Network
  1396. 3.33, 0.47, [3, 4, 3] [5, 4, 5]Multi-Scale Stacked Hourglass Network for Human Pose Estimation
  1397. 3.33, 0.47, [3, 4, 3] [5, 4, 5]A quantifiable testing of global translational invariance in Convolutional and Capsule Networks
  1398. 3.33, 0.47, [3, 4, 3] [5, 5, 4]MAJOR-MINOR LSTMS FOR WORD-LEVEL LANGUAGE MODEL
  1399. 3.33, 0.47, [3, 3, 4] [4, 4, 4]Deep models calibration with bayesian neural networks
  1400. 3.33, 0.94, [4, 4, 2] [4, 3, 4]BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating
  1401. 3.33, 1.25, [3, 2, 5] [4, 5, 3]Gradient Acceleration in Activation Functions
  1402. 3.33, 0.47, [4, 3, 3] [5, 5, 4]BEHAVIOR MODULE IN NEURAL NETWORKS
  1403. 3.33, 0.47, [3, 4, 3] [4, 3, 4]Neural Random Projections for Language Modelling
  1404. 3.33, 0.47, [3, 4, 3] [4, 4, 5]Step-wise Sensitivity Analysis: Identifying Partially Distributed Representations for Interpretable Deep Learning
  1405. 3.33, 0.94, [2, 4, 4] [4, 4, 3]Deconfounding Reinforcement Learning
  1406. 3.33, 0.94, [2, 4, 4] [5, 4, 4]Detecting Topological Defects in 2D Active Nematics Using Convolutional Neural Networks
  1407. 3.33, 0.47, [3, 3, 4] [3, 4, 5]Neural Distribution Learning for generalized time-to-event prediction
  1408. 3.33, 0.47, [4, 3, 3] [3, 4, 5]Beyond Games: Bringing Exploration to Robots in Real-world
  1409. 3.33, 0.47, [3, 4, 3] [4, 5, 4]Empirical Study of Easy and Hard Examples in CNN Training
  1410. 3.33, 1.70, [1, 5, 4] [4, 3, 4]Deterministic Policy Gradients with General State Transitions
  1411. 3.33, 0.47, [4, 3, 3] [4, 5, 4]Neural Network Regression with Beta, Dirichlet, and Dirichlet-Multinomial Outputs
  1412. 3.33, 0.47, [3, 3, 4] [2, 4, 3]ATTACK GRAPH CONVOLUTIONAL NETWORKS BY ADDING FAKE NODES
  1413. 3.33, 1.25, [3, 2, 5] [4, 5, 2]Generative model based on minimizing exact empirical Wasserstein distance
  1414. 3.33, 1.25, [5, 2, 3] [3, 2, 5]Learning powerful policies and better dynamics models by encouraging consistency
  1415. 3.33, 0.47, [3, 4, 3] [5, 3, 4]Non-Synergistic Variational Autoencoders
  1416. 3.33, 1.25, [5, 2, 3] [3, 5, 5]Uncertainty in Multitask Transfer Learning
  1417. 3.33, 1.25, [5, 2, 3] [3, 4, 3]The Conditional Entropy Bottleneck
  1418. 3.33, 0.47, [4, 3, 3] [3, 3, 4]Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
  1419. 3.33, 0.47, [4, 3, 3] [4, 2, 4]Combining adaptive algorithms and hypergradient method: a performance and robustness study
  1420. 3.00, 0.82, [2, 4, 3] [4, 4, 5]ATTENTION INCORPORATE NETWORK: A NETWORK CAN ADAPT VARIOUS DATA SIZE
  1421. 3.00, 0.00, [3, 3, 3] [5, 4, 3]Nonlinear Channels Aggregation Networks for Deep Action Recognition
  1422. 3.00, 0.82, [4, 2, 3] [5, 5, 4]Hybrid Policies Using Inverse Rewards for Reinforcement Learning
  1423. 3.00, 0.82, [2, 4, 3] [4, 4, 5]An Exhaustive Analysis of Lazy vs. Eager Learning Methods for Real-Estate Property Investment
  1424. 3.00, 0.82, [2, 4, 3] [5, 5, 5]Stacking for Transfer Learning
  1425. 3.00, 0.00, [3, 3, 3] [5, 3, 4]Mapping the hyponymy relation of wordnet onto vector Spaces
  1426. 3.00, 0.82, [2, 4, 3] [5, 4, 4]ReNeg and Backseat Driver: Learning from demonstration with continuous human feedback
  1427. 3.00, 0.00, [3, 3, 3] [3, 4, 3]Real-time Neural-based Input Method
  1428. 3.00, 1.00, [4, 2] [5, 4]FROM DEEP LEARNING TO DEEP DEDUCING: AUTOMATICALLY TRACKING DOWN NASH EQUILIBRIUM THROUGH AUTONOMOUS NEURAL AGENT, A POSSIBLE MISSING STEP TOWARD GENERAL A.I.
  1429. 3.00, 0.82, [4, 2, 3] [4, 2, 1]Learning of Sophisticated Curriculums by viewing them as Graphs over Tasks
  1430. 3.00, 0.00, [3, 3, 3] [5, 4, 5]iRDA Method for Sparse Convolutional Neural Networks
  1431. 3.00, 0.82, [3, 4, 2] [2, 4, 5]Geometry of Deep Convolutional Networks
  1432. 3.00, 0.82, [4, 2, 3] [5, 4, 4]Calibration of neural network logit vectors to combat adversarial attacks
  1433. 3.00, 0.82, [2, 4, 3] [4, 2, 4]Probabilistic Program Induction for Intuitive Physics Game Play
  1434. 3.00, 0.00, [3, 3, 3] [4, 3, 2]An Analysis of Composite Neural Network Performance from Function Composition Perspective
  1435. 3.00, 0.00, [3, 3, 3] [3, 2, 4]Dopamine: A Research Framework for Deep Reinforcement Learning
  1436. 3.00, 0.82, [3, 2, 4] [4, 4, 4]Learning with Reflective Likelihoods
  1437. 3.00, 1.41, [4, 1, 4] [5, 4, 3]Variational Autoencoders for Text Modeling without Weakening the Decoder
  1438. 3.00, 0.82, [4, 3, 2] [4, 3, 4]Evaluation Methodology for Attacks Against Confidence Thresholding Models
  1439. 3.00, 0.00, [3, 3, 3] [4, 3, 3]A NON-LINEAR THEORY FOR SENTENCE EMBEDDING
  1440. 3.00, 0.82, [4, 3, 2] [4, 3, 5]Learn From Neighbour: A Curriculum That Train Low Weighted Samples By Imitating
  1441. 3.00, 0.00, [3, 3, 3] [4, 4, 4]One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
  1442. 3.00, 0.82, [4, 3, 2] [2, 3, 4]Feature quantization for parsimonious and interpretable predictive models
  1443. 3.00, 0.00, [3, 3, 3] [4, 5, 4]Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference
  1444. 3.00, 0.82, [2, 3, 4] [5, 4, 4]HR-TD: A Regularized TD Method to Avoid Over-Generalization
  1445. 3.00, 0.82, [4, 3, 2] [4, 4, 1]HANDLING CONCEPT DRIFT IN WIFI-BASED INDOOR LOCALIZATION USING REPRESENTATION LEARNING
  1446. 3.00, 0.82, [2, 3, 4] [3, 3, 4]A Rate-Distortion Theory of Adversarial Examples
  1447. 3.00, 0.82, [2, 3, 4] [5, 5, 3]Classification in the dark using tactile exploration
  1448. 3.00, 0.00, [3, 3, 3] [4, 5, 4]End-to-End Multi-Lingual Multi-Speaker Speech Recognition
  1449. 3.00, 0.82, [2, 3, 4] [4, 5, 5]A Self-Supervised Method for Mapping Human Instructions to Robot Policies
  1450. 3.00, 0.82, [2, 3, 4] [3, 4, 4]ATTENTIVE EXPLAINABILITY FOR PATIENT TEMPO- RAL EMBEDDING
  1451. 3.00, 0.00, [3, 3, 3] [4, 5, 5]From Amortised to Memoised Inference: Combining Wake-Sleep and Variational-Bayes for Unsupervised Few-Shot Program Learning
  1452. 2.75, 0.83, [4, 2, 3, 2] [5, 5, 3, 4]Predictive Local Smoothness for Stochastic Gradient Methods
  1453. 2.67, 0.94, [4, 2, 2] [4, 5, 4]Multiple Encoder-Decoders Net for Lane Detection
  1454. 2.67, 0.47, [2, 3, 3] [5, 2, 4]Explaining Adversarial Examples with Knowledge Representation
  1455. 2.67, 1.25, [4, 1, 3] [5, 5, 2]Weak contraction mapping and optimization
  1456. 2.67, 0.47, [2, 3, 3] [5, 3, 5]Exponentially Decaying Flows for Optimization in Deep Learning
  1457. 2.67, 0.94, [2, 2, 4] [5, 4, 3]VARIATIONAL SGD: DROPOUT , GENERALIZATION AND CRITICAL POINT AT THE END OF CONVEXITY
  1458. 2.67, 0.47, [2, 3, 3] [5, 3, 5]Faster Training by Selecting Samples Using Embeddings
  1459. 2.67, 0.47, [3, 2, 3] [5, 5, 4]Decoupling Gating from Linearity
  1460. 2.67, 0.47, [2, 3, 3] [5, 4, 3]End-to-End Learning of Video Compression Using Spatio-Temporal Autoencoders
  1461. 2.67, 2.36, [6, 1, 1] [5, 5, 5]How Powerful are Graph Neural Networks?
  1462. 2.67, 0.94, [4, 2, 2] [4, 4, 4]A bird’s eye view on coherence, and a worm’s eye view on cohesion
  1463. 2.67, 0.47, [3, 3, 2] [5, 4, 4]HAPPIER: Hierarchical Polyphonic Music Generative RNN
  1464. 2.67, 1.25, [3, 1, 4] [4, 4, 3]Learning Goal-Conditioned Value Functions with one-step Path rewards rather than Goal-Rewards
  1465. 2.67, 0.47, [2, 3, 3] [2, 2, 3]A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS
  1466. 2.50, 0.50, [2, 3] [3, 4]A Solution to China Competitive Poker Using Deep Learning
  1467. 2.33, 0.94, [3, 1, 3] [5, 5, 5]Training Variational Auto Encoders with Discrete Latent Representations using Importance Sampling
  1468. 2.33, 0.94, [1, 3, 3] [3, 4, 3]Psychophysical vs. learnt texture representations in novelty detection
  1469. 2.33, 0.94, [3, 1, 3] [5, 5, 3]Pixel Chem: A Representation for Predicting Material Properties with Neural Network
  1470. 2.33, 0.47, [3, 2, 2] [4, 5, 5]VECTORIZATION METHODS IN RECOMMENDER SYSTEM
  1471. 2.33, 0.47, [3, 2, 2] [5, 5, 4]Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space
  1472. 2.33, 1.89, [5, 1, 1] [4, 5, 5]Advanced Neuroevolution: A gradient-free algorithm to train Deep Neural Networks
  1473. 2.33, 0.47, [2, 2, 3] [3, 4, 3]Hierarchical Deep Reinforcement Learning Agent with Counter Self-play on Competitive Games
  1474. 2.25, 0.43, [2, 2, 3, 2] [3, 3, 3, 4]A Synaptic Neural Network and Synapse Learning
  1475. 2.00, 0.63, [1, 2, 2, 3, 2] [5, 4, 5, 4, 5]Hierarchical Bayesian Modeling for Clustering Sparse Sequences in the Context of Group Profiling
  1476. 1.50, 0.50, [2, 1, 2, 1] [5, 5, 5, 5]Object detection deep learning networks for Optical Character Recognition

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