# ICLR 2019: open review links

tylenol canada confer 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.

tail http://wizardofroz.ca/62524-zoloft-price.html 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

refer https://pacificconservatory.com/81091-nurofen-price.html 2 to 5 reviewers per paper, but usually 3 to 4.

synthroid uk cater 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.

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

http://sunnycycleps.com/?author=20 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.

buy augmentin cheap 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