ICLR 2017
is taking place today in Toulon this week, there will be a blog post for each
half day that features directly links to papers and attendant codes if there are any. The meeting will be featured live on Facebook here at: https://www.facebook.com/iclr.cc/ . If you want to say hi, I am around.
Afternoon Session – Session Chair: Joan Bruna (sponsored by Baidu)
14.30 - 15.10 Invited talk 2: Benjamin Recht
15.10 - 15.30 Contributed Talk 3: Understanding deep learning requires rethinking generalization - BEST PAPER AWARD
15.30 - 15.50 Contributed Talk 4: Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
15.50 - 16.10 Contributed Talk 5: Towards Principled Methods for Training Generative Adversarial Networks
16.10 - 16.30 Coffee Break
16.30 - 18.20 Poster Session 2 (Conference Papers, Workshop Papers)
18.20 - 18.30 Group photo at stadium attached to Neptune Congress Center.
15.10 - 15.30 Contributed Talk 3: Understanding deep learning requires rethinking generalization - BEST PAPER AWARD
15.30 - 15.50 Contributed Talk 4: Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
15.50 - 16.10 Contributed Talk 5: Towards Principled Methods for Training Generative Adversarial Networks
16.10 - 16.30 Coffee Break
16.30 - 18.20 Poster Session 2 (Conference Papers, Workshop Papers)
18.20 - 18.30 Group photo at stadium attached to Neptune Congress Center.
C1: Neuro-Symbolic Program Synthesis
C2: Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy (code)
C3: Trained Ternary Quantization (code)
C4: DSD: Dense-Sparse-Dense Training for Deep Neural Networks (code)
C5: A Compositional Object-Based Approach to Learning Physical Dynamics (code, project site)
C6: Multilayer Recurrent Network Models of Primate Retinal Ganglion Cells
C7: Improving Generative Adversarial Networks with Denoising Feature Matching (chainer implementation)
C8: Transfer of View-manifold Learning to Similarity Perception of Novel Objects
C9: What does it take to generate natural textures?
C10: Emergence of foveal image sampling from learning to attend in visual scenes
C11: PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications
C12: Learning to Optimize
C13: Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
C14: Optimal Binary Autoencoding with Pairwise Correlations
C15: On the Quantitative Analysis of Decoder-Based Generative Models (evaluation code)
C16: Adversarial machine learning at scale
C17: Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
C18: Capacity and Learnability in Recurrent Neural Networks
C19: Deep Learning with Dynamic Computation Graphs (TensorFlow code)
C20: Exploring Sparsity in Recurrent Neural Networks
C21: Structured Attention Networks (code)
C22: Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
C23: Variational Lossy Autoencoder
C24: Learning to Query, Reason, and Answer Questions On Ambiguous Texts
C25: Deep Biaffine Attention for Neural Dependency Parsing
C26: A Compare-Aggregate Model for Matching Text Sequences (code)
C27: Data Noising as Smoothing in Neural Network Language Models
C28: Neural Variational Inference For Topic Models
C29: Bidirectional Attention Flow for Machine Comprehension (code, page)
C30: Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
C31: Stochastic Neural Networks for Hierarchical Reinforcement Learning
C32: Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning (video)
C33: Third Person Imitation Learning
C2: Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy (code)
C3: Trained Ternary Quantization (code)
C4: DSD: Dense-Sparse-Dense Training for Deep Neural Networks (code)
C5: A Compositional Object-Based Approach to Learning Physical Dynamics (code, project site)
C6: Multilayer Recurrent Network Models of Primate Retinal Ganglion Cells
C7: Improving Generative Adversarial Networks with Denoising Feature Matching (chainer implementation)
C8: Transfer of View-manifold Learning to Similarity Perception of Novel Objects
C9: What does it take to generate natural textures?
C10: Emergence of foveal image sampling from learning to attend in visual scenes
C11: PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications
C12: Learning to Optimize
C13: Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
C14: Optimal Binary Autoencoding with Pairwise Correlations
C15: On the Quantitative Analysis of Decoder-Based Generative Models (evaluation code)
C16: Adversarial machine learning at scale
C17: Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
C18: Capacity and Learnability in Recurrent Neural Networks
C19: Deep Learning with Dynamic Computation Graphs (TensorFlow code)
C20: Exploring Sparsity in Recurrent Neural Networks
C21: Structured Attention Networks (code)
C22: Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
C23: Variational Lossy Autoencoder
C24: Learning to Query, Reason, and Answer Questions On Ambiguous Texts
C25: Deep Biaffine Attention for Neural Dependency Parsing
C26: A Compare-Aggregate Model for Matching Text Sequences (code)
C27: Data Noising as Smoothing in Neural Network Language Models
C28: Neural Variational Inference For Topic Models
C29: Bidirectional Attention Flow for Machine Comprehension (code, page)
C30: Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
C31: Stochastic Neural Networks for Hierarchical Reinforcement Learning
C32: Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning (video)
C33: Third Person Imitation Learning
W1: Audio Super-Resolution using Neural Networks (code)
W2: Semantic embeddings for program behaviour patterns
W3: De novo drug design with deep generative models : an empirical study
W4: Memory Matching Networks for Genomic Sequence Classification
W5: Char2Wav: End-to-End Speech Synthesis
W6: Fast Chirplet Transform Injects Priors in Deep Learning of Animal Calls and Speech
W7: Weight-averaged consistency targets improve semi-supervised deep learning results
W8: Particle Value Functions
W9: Out-of-class novelty generation: an experimental foundation
W10: Performance guarantees for transferring representations (presentation, video)
W11: Generative Adversarial Learning of Markov Chains
W12: Short and Deep: Sketching and Neural Networks
W13: Understanding intermediate layers using linear classifier probes
W14: Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity
W15: Neural Combinatorial Optimization with Reinforcement Learning (TensorFlow code)
W16: Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents
W17: Adversarial Discriminative Domain Adaptation (workshop extended abstract)
W18: Efficient Sparse-Winograd Convolutional Neural Networks
W19: Neural Expectation Maximization
W2: Semantic embeddings for program behaviour patterns
W3: De novo drug design with deep generative models : an empirical study
W4: Memory Matching Networks for Genomic Sequence Classification
W5: Char2Wav: End-to-End Speech Synthesis
W6: Fast Chirplet Transform Injects Priors in Deep Learning of Animal Calls and Speech
W7: Weight-averaged consistency targets improve semi-supervised deep learning results
W8: Particle Value Functions
W9: Out-of-class novelty generation: an experimental foundation
W10: Performance guarantees for transferring representations (presentation, video)
W11: Generative Adversarial Learning of Markov Chains
W12: Short and Deep: Sketching and Neural Networks
W13: Understanding intermediate layers using linear classifier probes
W14: Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity
W15: Neural Combinatorial Optimization with Reinforcement Learning (TensorFlow code)
W16: Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents
W17: Adversarial Discriminative Domain Adaptation (workshop extended abstract)
W18: Efficient Sparse-Winograd Convolutional Neural Networks
W19: Neural Expectation Maximization
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