ICLR 2017

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iclr2017:conference_posters [2017/03/28 08:17]
rnogueira
iclr2017:conference_posters [2017/04/23 09:26] (current)
hugo
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 Below are the Conference Track papers presented at each of the poster sessions (on Monday, Tuesday or Wednesday, in the morning or evening). To find a paper, look for the poster with the corresponding number in the area dedicated to the Conference Track. Below are the Conference Track papers presented at each of the poster sessions (on Monday, Tuesday or Wednesday, in the morning or evening). To find a paper, look for the poster with the corresponding number in the area dedicated to the Conference Track.
  
-===Monday Morning (April 24th, 10:30am to 12:30pm)=== +======Note to the Presenters======= 
-  - Making Neural Programming Architectures Generalize via Recursion +Each poster panel is 2 meters large and 1 meter tall.\\ 
-  - Learning Graphical State Transitions +If neededtape will be provided ​to fix your poster.
-  - Distributed Second-Order Optimization using Kronecker-Factored Approximations +
-  - Normalizing the Normalizers:​ Comparing and Extending Network Normalization Schemes +
-  - Neural Program Lattices +
-  - Diet Networks: Thin Parameters for Fat Genomics +
-  - Unsupervised Cross-Domain Image Generation +
-  - Towards Principled Methods for Training Generative Adversarial Networks +
-  - Recurrent Mixture Density Network for Spatiotemporal Visual Attention +
-  - Paying More Attention ​to Attention: Improving ​the Performance of Convolutional Neural Networks via Attention Transfer +
-  - Pruning Filters for Efficient ConvNets +
-  - Optimization as a Model for Few-Shot Learning +
-  - Understanding deep learning requires rethinking generalization +
-  - On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima +
-  - Recurrent Hidden Semi-Markov Model +
-  - Nonparametric Neural Networks +
-  - Learning to Generate Samples from Noise through Infusion Training +
-  - An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax +
-  - Highway and Residual Networks learn Unrolled Iterative Estimation +
-  - Soft Weight-Sharing for Neural Network Compression +
-  - Snapshot Ensembles: Train 1Get M for Free +
-  - Towards a Neural Statistician +
-  - Learning Curve Prediction with Bayesian Neural Networks +
-  - Learning End-to-End Goal-Oriented Dialog +
-  - Multi-Agent Cooperation and the Emergence of (Natural) Language +
-  - Efficient Vector Representation for Documents through Corruption +
-  - Improving Neural Language Models with a Continuous Cache +
-  - Program Synthesis for Character Level Language Modeling +
-  - Tracking the World State with Recurrent Entity Networks +
-  - Reinforcement Learning with Unsupervised Auxiliary Tasks +
-  - Neural Architecture Search with Reinforcement Learning +
-  - Sample Efficient Actor-Critic with Experience Replay +
-  - Learning to Act by Predicting the Future+
  
-===Monday Afternoon (April 24th, 4:30pm to 6:30pm)=== 
-  - Neuro-Symbolic Program Synthesis 
-  - Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy 
-  - Trained Ternary Quantization 
-  - DSD: Dense-Sparse-Dense Training for Deep Neural Networks 
-  - A Compositional Object-Based Approach to Learning Physical Dynamics 
-  - Multilayer Recurrent Network Models of Primate Retinal Ganglion Cells 
-  - Improving Generative Adversarial Networks with Denoising Feature Matching 
-  - Transfer of View-manifold Learning to Similarity Perception of Novel Objects 
-  - What does it take to generate natural textures? 
-  - Emergence of foveal image sampling from learning to attend in visual scenes 
-  - PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications 
-  - Learning to Optimize 
-  - Training Compressed Fully-Connected Networks with a Density-Diversity Penalty 
-  - Optimal Binary Autoencoding with Pairwise Correlations 
-  - On the Quantitative Analysis of Decoder-Based Generative Models 
-  - Learning to Remember Rare Events 
-  - Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks 
-  - Capacity and Learnability in Recurrent Neural Networks 
-  - Deep Learning with Dynamic Computation Graphs 
-  - Exploring Sparsity in Recurrent Neural Networks 
-  - Structured Attention Networks 
-  - Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations 
-  - Variational Lossy Autoencoder 
-  - Learning to Query, Reason, and Answer Questions On Ambiguous Texts 
-  - Deep Biaffine Attention for Neural Dependency Parsing 
-  - A Compare-Aggregate Model for Matching Text Sequences 
-  - Data Noising as Smoothing in Neural Network Language Models 
-  - Neural Variational Inference For Topic Models 
-  - Words or Characters? Fine-grained Gating for Reading Comprehension 
-  - Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic 
-  - Stochastic Neural Networks for Hierarchical Reinforcement Learning 
-  - Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning 
-  - Third Person Imitation Learning 
  
-===Tuesday ​Morning (April ​25th, 10:30am to 12:​30pm)=== +<​html><​div id='​monday_morning'></​div></​html>​ 
-  - DeepDSLA Compilation-based Domain-Specific Language for Deep Learning +====Monday ​Morning (April ​24th, 10:30am to 12:30pm)==== 
-  - SampleRNNAn Unconditional End-to-End Neural Audio Generation Model +C1: Making Neural Programming Architectures Generalize via Recursion\\ 
-  - Deep Probabilistic Programming +C2: Learning ​Graphical State Transitions\\ 
-  - Lie-Access ​Neural ​Turing Machines +C3Distributed Second-Order Optimization using Kronecker-Factored Approximations\\ 
-  - Learning Features of Music From Scratch +C4: Normalizing the Normalizers:​ Comparing and Extending Network Normalization Schemes\\ 
-  - Mode Regularized Generative Adversarial ​Networks +C5: Neural ​Program Lattices\\ 
-  End-to-end Optimized ​Image Compression +C6: Diet Networks: Thin Parameters for Fat Genomics\\ 
-  - Variational Recurrent ​Adversarial ​Deep Domain Adaptation +C7: Unsupervised Cross-Domain ​Image Generation\\ 
-  - Steerable CNNs +C8: Towards Principled Methods for Training Generative ​Adversarial Networks\\ 
-  - Deep Predictive Coding ​Networks ​for Video Prediction and Unsupervised Learning +C9Recurrent Mixture Density Network ​for Spatiotemporal Visual Attention\\ 
-  - PixelVAEA Latent Variable Model for Natural Images +C10: Paying More Attention to Attention: Improving the Performance of Convolutional ​Neural Networks ​via Attention Transfer\\ 
-  - A recurrent neural network without chaos +C11Pruning Filters for Efficient ConvNets\\ 
-  - Outrageously Large Neural Networks: ​The Sparsely-Gated Mixture-of-Experts Layer +C12: Stick-Breaking Variational Autoencoders\\ 
-  Tree-structured decoding with doubly-recurrent neural networks +C13Identity Matters in Deep Learning\\ 
-  - Introspection:Accelerating Neural Network Training By Learning ​Weight Evolution +C14On Large-Batch Training ​for Deep Learning: Generalization Gap and Sharp Minima\\ 
-  - HyperbandBandit-Based Configuration Evaluation ​for Hyperparameter Optimization +C15: Recurrent ​Hidden Semi-Markov Model\\ 
-  - Quasi-Recurrent Neural Networks +C16: Nonparametric ​Neural Networks\\ 
-  - Attend, Adapt and TransferAttentive Deep Architecture for Adaptive Transfer ​from multiple sources in the same domain +C17Learning to Generate Samples ​from Noise through Infusion Training\\ 
-  A Baseline ​for Detecting Misclassified ​and Out-of-Distribution Examples in Neural Networks +C18: An Information-Theoretic Framework ​for Fast and Robust Unsupervised Learning via Neural ​Population Infomax\\ 
-  Trusting SVM for Piecewise Linear CNNs +C19: Highway and Residual ​Networks ​learn Unrolled Iterative Estimation\\ 
-  - Maximum Entropy Flow Networks +C20: Soft Weight-Sharing ​for Neural Network Compression\\ 
-  - The Concrete DistributionA Continuous Relaxation of Discrete Random Variables +C21: Snapshot Ensembles: Train 1, Get M for Free\\ 
-  - Unrolled Generative Adversarial ​Networks +C22Towards a Neural Statistician\\ 
-  - A Simple but Tough-to-Beat Baseline for Sentence Embeddings +C23: Learning Curve Prediction with Bayesian Neural ​Networks\\ 
-  ​Query-Reduction Networks for Question Answering +C24: Learning End-to-End Goal-Oriented Dialog\\ 
-  Machine Comprehension Using Match-LSTM ​and Answer Pointer +C25: Multi-Agent Cooperation ​and the Emergence of (Natural) Language\\ 
-  - Bidirectional Attention Flow for Machine Comprehension +C26: Efficient Vector Representation ​for Documents through Corruption\\ 
-  - Dynamic Coattention Networks For Question Answering +C27: Improving ​Neural ​Language Models with a Continuous Cache\\ 
-  - Multi-view Recurrent ​Neural ​Acoustic Word Embeddings +C28: Program Synthesis ​for Character Level Language Modeling\\ 
-  - Episodic Exploration for Deep Deterministic Policies ​for StarCraft Micromanagement +C29: Tracking the World State with Recurrent Entity Networks\\ 
-  - Training Agent for First-Person Shooter Game with Actor-Critic Curriculum ​Learning +C30: Reinforcement ​Learning ​with Unsupervised Auxiliary Tasks\\ 
-  - Generalizing Skills ​with Semi-Supervised ​Reinforcement Learning +C31: Neural Architecture Search ​with Reinforcement Learning\\ 
-  Improving Policy Gradient ​by Exploring Under-appreciated Rewards+C32: Sample Efficient Actor-Critic with Experience Replay\\ 
 +C33: Learning to Act by Predicting the Future\\
  
-===Tuesday ​Afternoon (April ​25th, 4:30pm to 6:​30pm)=== +<​html><​div id='​monday_afternoon'></​div></​html>​ 
-  Sigma Delta Quantized Networks +====Monday ​Afternoon (April ​24th, 4:30pm to 6:30pm)==== 
-  - PaleoA Performance ​Model for Deep Neural Networks +C1: Neuro-Symbolic Program Synthesis\\ 
-  - DeepCoderLearning to Write Programs +C2Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy\\ 
-  - Topology and Geometry of Deep Rectified Network Optimization Landscapes +C3Trained Ternary Quantization\\ 
-  - Incremental Network QuantizationTowards Lossless CNNs with Low-precision Weights +C4DSD: Dense-Sparse-Dense Training for Deep Neural Networks\\ 
-  ​Learning to Perform Physics Experiments via Deep Reinforcement Learning +C5: A Compositional Object-Based Approach to Learning Physical Dynamics\\ 
-  - Decomposing Motion and Content for Natural Video Sequence Prediction +C6: Multilayer Recurrent Network Models of Primate Retinal Ganglion Cells\\ 
-  - Calibrating Energy-based Generative Adversarial Networks +C7Improving ​Generative Adversarial Networks ​with Denoising Feature Matching\\ 
-  - Pruning Convolutional ​Neural Networks ​for Resource Efficient Inference +C8: Transfer of View-manifold ​Learning ​to Similarity Perception of Novel Objects\\ 
-  Incorporating long-range consistency in CNN-based texture generation +C9What does it take to generate natural textures?​\\ 
-  - Lossy Image Compression with Compressive Autoencoders +C10: Emergence ​of foveal image sampling ​from learning to attend in visual scenes\\ 
-  - LR-GANLayered Recursive ​Generative Adversarial Networks ​for Image Generation +C11: PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications\\ 
-  Semi-supervised Knowledge Transfer for Deep Learning ​from Private Training Data +C12: Learning ​to Optimize\\ 
-  - Deep Variational Bayes FiltersUnsupervised Learning ​of State Space Models ​from Raw Data +C13: Do Deep Convolutional Nets Really Need to be Deep and Convolutional?​\\ 
-  - Mollifying Networks +C14: Optimal Binary Autoencoding ​with Pairwise Correlations\\ 
-  - beta-VAE: Learning ​Basic Visual Concepts with a Constrained Variational Framework +C15: On the Quantitative Analysis of Decoder-Based Generative Models\\ 
-  - Categorical Reparameterization ​with Gumbel-Softmax +C16: Adversarial machine learning at scale\\ 
-  Online Bayesian ​Transfer Learning for Sequential Data Modeling +C17: Transfer Learning for Sequence ​Tagging with Hierarchical Recurrent Networks\\ 
-  - Latent ​Sequence ​Decompositions +C18: Capacity and Learnability in Recurrent ​Neural Networks\\ 
-  - Density estimation using Real NVP +C19Deep Learning ​with Dynamic ​Computation ​Graphs\\ 
-  - Recurrent ​Batch Normalization +C20: Exploring Sparsity ​in Recurrent Neural Networks\\ 
-  - SGDRStochastic Gradient Descent ​with Restarts +C21: Structured Attention Networks\\ 
-  - Variable ​Computation in Recurrent Neural Networks +C22: Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning\\ 
-  ​- ​Deep Variational ​Information Bottleneck +C23: Variational ​Lossy Autoencoder\\ 
-  - A SELF-ATTENTIVE SENTENCE EMBEDDING +C24: Learning to Query, Reason, and Answer Questions On Ambiguous Texts\\ 
-  - TopicRNNA Recurrent ​Neural ​Network with Long-Range Semantic ​Dependency +C25Deep Biaffine Attention for Neural Dependency ​Parsing\\ 
-  Frustratingly Short Attention Spans in Neural Language ​Modeling +C26: A Compare-Aggregate Model for Matching Text Sequences\\ 
-  - Offline Bilingual Word Vectors Without a Dictionary +C27: Data Noising as Smoothing ​in Neural ​Network ​Language ​Models\\ 
-  - LEARNING A NATURAL LANGUAGE INTERFACE WITH NEURAL PROGRAMMER +C28: Neural Variational Inference For Topic Models\\ 
-  Designing ​Neural ​Network Architectures using Reinforcement Learning +C29: Bidirectional Attention Flow for Machine Comprehension\\ 
-  - Metacontrol for Adaptive Imagination-Based Optimization +C30: Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic\\ 
-  - Recurrent Environment Simulators +C31: Stochastic ​Neural ​Networks for Hierarchical ​Reinforcement Learning\\ 
-  - EPOpt: Learning ​Robust Neural Network Policies Using Model Ensembles+C32: Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning\\ 
 +C33Third Person Imitation ​Learning\\
  
-===Wednesday ​Morning (April ​26th, 10:30am to 12:​30pm)=== +<​html><​div id='​tuesday_morning'></​div></​html>​ 
-  Deep Multi-task Representation ​Learning: A Tensor Factorisation Approach +====Tuesday ​Morning (April ​25th, 10:30am to 12:30pm)==== 
-  ​Training deep neural-networks using a noise adaptation layer +C1: DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning\\ 
-  - Delving into Transferable Adversarial Examples and Black-box Attacks +C2: A SELF-ATTENTIVE SENTENCE EMBEDDING\\ 
-  Towards the Limit of Network Quantization +C3: Deep Probabilistic Programming\\ 
-  - Towards Deep Interpretability (MUS-ROVER II): Learning ​Hierarchical Representations ​of Tonal Music +C4: Lie-Access Neural Turing Machines\\ 
-  Learning ​to superoptimize programs +C5: Learning ​Features ​of Music From Scratch\\ 
-  ​Regularizing CNNs with Locally Constrained Decorrelations +C6: Mode Regularized Generative Adversarial Networks\\ 
-  - Generative Multi-Adversarial ​Networks +C7: End-to-end Optimized Image Compression\\ 
-  - Visualizing Deep Neural Network DecisionsPrediction Difference Analysis +C8: Variational Recurrent ​Adversarial ​Deep Domain Adaptation\\ 
-  - FractalNetUltra-Deep Neural ​Networks without ​Residuals +C9Steerable CNNs\\ 
-  Faster CNNs with Direct Sparse Convolutions and Guided Pruning +C10: Deep Predictive Coding ​Networks ​for Video Prediction and Unsupervised Learning\\ 
-  ​FILTER SHAPING FOR CONVOLUTIONAL NEURAL NETWORKS +C11: PixelVAE: A Latent Variable Model for Natural Images\\ 
-  The Neural ​Noisy Channel +C12: A recurrent neural network ​without ​chaos\\ 
-  Automatic Rule Extraction from Long Short Term Memory Networks +C13: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer\\ 
-  Adversarially Learned Inference +C14: Tree-structured decoding with doubly-recurrent neural networks\\ 
-  ​- ​Deep Information Propagation +C15: Introspection:​Accelerating ​Neural ​Network Training By Learning Weight Evolution\\ 
-  Revisiting Classifier Two-Sample Tests +C16: Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization\\ 
-  - Loss-aware Binarization of Deep Networks +C17: Quasi-Recurrent Neural Networks\\ 
-  - Energy-based ​Generative Adversarial Networks +C18: Attend, Adapt and Transfer: Attentive ​Deep Architecture for Adaptive Transfer from multiple sources in the same domain\\ 
-  Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning +C19: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks\\ 
-  Temporal Ensembling ​for Semi-Supervised Learning +C20: Trusting SVM for Piecewise Linear CNNs\\ 
-  On Detecting Adversarial Perturbations +C21: Maximum Entropy Flow Networks\\ 
-  - Identity Matters in Deep Learning +C22: The Concrete Distribution:​ A Continuous Relaxation of Discrete Random Variables\\ 
-  - Adversarial Feature Learning +C23: Unrolled ​Generative Adversarial Networks\\ 
-  - Learning through Dialogue Interactions +C24: A Simple but Tough-to-Beat Baseline for Sentence Embeddings\\ 
-  - Learning to Compose ​Words into Sentences with Reinforcement Learning +C25: Query-Reduction Networks ​for Question Answering\\ 
-  ​Batch Policy Gradient Methods ​for Improving Neural Conversation Models +C26: Machine Comprehension Using Match-LSTM and Answer Pointer\\ 
-  Tying Word Vectors and Word ClassifiersA Loss Framework ​for Language Modeling +C27: Words or Characters? Fine-grained Gating ​for Reading Comprehension\\ 
-  - Geometry of Polysemy +C28: Dynamic Coattention Networks For Question Answering\\ 
-  - PGQCombining policy gradient and Q-learning +C29: Multi-view Recurrent Neural Acoustic ​Word Embeddings\\ 
-  - Reinforcement Learning through Asynchronous Advantage ​Actor-Critic ​on a GPU +C30Episodic Exploration ​for Deep Deterministic Policies for StarCraft Micromanagement\\ 
-  - Learning ​to Navigate in Complex Environments +C31Training Agent for First-Person Shooter Game with Actor-Critic ​Curriculum Learning\\ 
-  Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks+C32: Generalizing Skills with Semi-Supervised Reinforcement ​Learning\\ 
 +C33: Improving Policy Gradient by Exploring Under-appreciated Rewards\\
  
-===Wednesday Afternoon (April 26th, 4:30pm to 6:​30pm)=== +<​html><​div id='​tuesday_afternoon'></​div></​html>​ 
-  ​- ​Learning recurrent representations for hierarchical behavior modeling +====Tuesday Afternoon (April 25th, 2:00pm to 4:​00pm)==== 
-  ​- ​Predicting Medications from Diagnostic Codes with Recurrent Neural Networks +C1: Sigma Delta Quantized Networks\\ 
-  ​- ​Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks +C2: Paleo: A Performance Model for Deep Neural Networks\\ 
-  ​- ​HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving +C3: DeepCoder: Learning to Write Programs\\ 
-  ​- ​Learning Invariant Representations Of Planar Curves +C4: Topology and Geometry of Deep Rectified Network Optimization Landscapes\\ 
-  ​- ​Entropy-SGD:​ Biasing Gradient Descent Into Wide Valleys +C5: Incremental Network Quantization:​ Towards Lossless CNNs with Low-precision Weights\\ 
-  ​- ​Amortised MAP Inference for Image Super-resolution +C6: Learning to Perform Physics Experiments via Deep Reinforcement Learning\\ 
-  ​- ​Inductive Bias of Deep Convolutional Networks through Pooling Geometry +C7: Decomposing Motion and Content for Natural Video Sequence Prediction\\ 
-  ​- ​Neural Photo Editing with Introspective Adversarial Networks +C8: Calibrating Energy-based Generative Adversarial Networks\\ 
-  ​- ​A Learned Representation For Artistic Style +C9: Pruning Convolutional Neural Networks for Resource Efficient Inference\\ 
-  - Adversarial Machine ​Learning ​at Scale +C10: Incorporating long-range consistency in CNN-based texture generation\\ 
-  Stick-Breaking Variational Autoencoders +C11: Lossy Image Compression with Compressive Autoencoders\\ 
-  ​- ​Support Regularized Sparse Coding and Its Fast Encoder +C12: LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation\\ 
-  ​- ​Discrete Variational Autoencoders +C13: Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data\\ 
-  Do Deep Convolutional Nets Really Need to be Deep and Convolutional?​ +C14: Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data\\ 
-  ​- Efficient Representation of Low-Dimensional Manifolds using Deep Networks +C15: Mollifying Networks\\ 
-  ​- ​Semi-Supervised Classification with Graph Convolutional Networks +C16: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\\ 
-  ​- ​Understanding Neural Sparse Coding with Matrix Factorization +C17: Categorical Reparameterization with Gumbel-Softmax\\ 
-  ​- ​Tighter bounds lead to improved classifiers +C18: Online Bayesian Transfer Learning for Sequential Data Modeling\\ 
-  ​- ​Why Deep Neural Networks for Function Approximation?​ +C19: Latent Sequence Decompositions\\ 
-  ​- ​Hierarchical Multiscale Recurrent Neural Networks +C20: Density estimation using Real NVP\\ 
-  ​- ​Dropout with Expectation-linear Regularization +C21: Recurrent Batch Normalization\\ 
-  ​- ​HyperNetworks +C22: SGDR: Stochastic Gradient Descent with Restarts\\ 
-  ​- ​Hadamard Product for Low-rank Bilinear Pooling +C23: Variable Computation in Recurrent Neural Networks\\ 
-  ​- ​Adversarial Training Methods for Semi-Supervised Text Classification +C24: Deep Variational Information Bottleneck\\ 
-  ​- ​Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks +C25: SampleRNN: An Unconditional End-to-End Neural Audio Generation Model\\ 
-  ​- ​Pointer Sentinel Mixture Models +C26: TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency\\ 
-  ​- ​Reasoning with Memory Augmented Neural Networks for Language Comprehension +C27: Frustratingly Short Attention Spans in Neural Language Modeling\\ 
-  ​- ​Dialogue Learning With Human-in-the-Loop +C28: Offline Bilingual Word Vectors, Orthogonal Transformations and the Inverted Softmax\\ 
-  - Learning to RepeatFine Grained Action Repetition for Deep Reinforcement Learning +C29: LEARNING A NATURAL LANGUAGE INTERFACE WITH NEURAL PROGRAMMER\\ 
-  ​- ​Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening +C30: Designing Neural Network Architectures using Reinforcement Learning\\ 
-  ​- ​Learning Visual Servoing with Deep Features and Trust Region Fitted Q-Iteration +C31: Metacontrol for Adaptive Imagination-Based Optimization\\ 
-  ​- ​An Actor-Critic Algorithm for Sequence Prediction+C32: Recurrent Environment Simulators\\ 
 +C33: EPOpt: Learning Robust Neural Network Policies Using Model Ensembles\\ 
 + 
 +<​html><​div id='​wednesday_morning'></​div></​html>​ 
 +====Wednesday Morning (April 26th, 10:30am to 12:​30pm)==== 
 +C1: Deep Multi-task Representation Learning: A Tensor Factorisation Approach\\ 
 +C2: Training deep neural-networks using a noise adaptation layer\\ 
 +C3: Delving into Transferable Adversarial Examples and Black-box Attacks\\ 
 +C4: Towards the Limit of Network Quantization\\ 
 +C5: Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music\\ 
 +C6: Learning to superoptimize programs\\ 
 +C7: Regularizing CNNs with Locally Constrained Decorrelations\\ 
 +C8: Generative Multi-Adversarial Networks\\ 
 +C9: Visualizing Deep Neural Network Decisions: Prediction Difference Analysis\\ 
 +C10: FractalNet: Ultra-Deep Neural Networks without Residuals\\ 
 +C11: Faster CNNs with Direct Sparse Convolutions and Guided Pruning\\ 
 +C12: FILTER SHAPING FOR CONVOLUTIONAL NEURAL NETWORKS\\ 
 +C13: The Neural Noisy Channel\\ 
 +C14: Automatic Rule Extraction from Long Short Term Memory Networks\\ 
 +C15: Adversarially Learned Inference\\ 
 +C16: Deep Information Propagation\\ 
 +C17: Revisiting Classifier Two-Sample Tests\\ 
 +C18: Loss-aware Binarization of Deep Networks\\ 
 +C19: Energy-based Generative Adversarial Networks\\ 
 +C20: Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning\\ 
 +C21: Temporal Ensembling for Semi-Supervised Learning\\ 
 +C22: On Detecting Adversarial Perturbations\\ 
 +C23: Understanding deep learning requires rethinking generalization\\ 
 +C24: Adversarial Feature Learning\\ 
 +C25: Learning through Dialogue Interactions\\ 
 +C26: Learning to Compose Words into Sentences with Reinforcement Learning\\ 
 +C27: Batch Policy Gradient Methods for Improving Neural Conversation Models\\ 
 +C28: Tying Word Vectors and Word Classifiers:​ A Loss Framework for Language Modeling\\ 
 +C29: Geometry of Polysemy\\ 
 +C30: PGQ: Combining policy gradient and Q-learning\\ 
 +C31: Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU\\ 
 +C32: Learning to Navigate in Complex Environments\\ 
 +C33: Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks\\ 
 + 
 +<​html><​div id='​wednesday_afternoon'></​div></​html>​ 
 +====Wednesday Afternoon (April 26th, 4:30pm to 6:30pm)==== 
 +C1: Learning recurrent representations for hierarchical behavior modeling\\ 
 +C2: Predicting Medications from Diagnostic Codes with Recurrent Neural Networks\\ 
 +C3: Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks\\ 
 +C4: HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving\\ 
 +C5: Learning Invariant Representations Of Planar Curves\\ 
 +C6: Entropy-SGD:​ Biasing Gradient Descent Into Wide Valleys\\ 
 +C7: Amortised MAP Inference for Image Super-resolution\\ 
 +C8: Inductive Bias of Deep Convolutional Networks through Pooling Geometry\\ 
 +C9: Neural Photo Editing with Introspective Adversarial Networks\\ 
 +C10: A Learned Representation For Artistic Style\\ 
 +C11: Learning ​to Remember Rare Events\\ 
 +C12: Optimization as a Model for Few-Shot Learning\\ 
 +C13: Support Regularized Sparse Coding and Its Fast Encoder\\ 
 +C14: Discrete Variational Autoencoders\\ 
 +C15: Training Compressed Fully-Connected Networks with a Density-Diversity Penalty\\ 
 +C16: Efficient Representation of Low-Dimensional Manifolds using Deep Networks\\ 
 +C17: Semi-Supervised Classification with Graph Convolutional Networks\\ 
 +C18: Understanding Neural Sparse Coding with Matrix Factorization\\ 
 +C19: Tighter bounds lead to improved classifiers\\ 
 +C20: Why Deep Neural Networks for Function Approximation?​\\ 
 +C21: Hierarchical Multiscale Recurrent Neural Networks\\ 
 +C22: Dropout with Expectation-linear Regularization\\ 
 +C23: HyperNetworks\\ 
 +C24: Hadamard Product for Low-rank Bilinear Pooling\\ 
 +C25: Adversarial Training Methods for Semi-Supervised Text Classification\\ 
 +C26: Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks\\ 
 +C27: Pointer Sentinel Mixture Models\\ 
 +C28: Reasoning with Memory Augmented Neural Networks for Language Comprehension\\ 
 +C29: Dialogue Learning With Human-in-the-Loop\\ 
 +C30Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations\\ 
 +C31: Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening\\ 
 +C32: Learning Visual Servoing with Deep Features and Trust Region Fitted Q-Iteration\\ 
 +C33: An Actor-Critic Algorithm for Sequence Prediction\\