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iclr2017:conference_posters [2017/03/28 08:32] 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. | ||

+ | |||

+ | ======Note to the Presenters======= | ||

+ | Each poster panel is 2 meters large and 1 meter tall.\\ | ||

+ | If needed, tape will be provided to fix your poster. | ||

+ | |||

<html><div id='monday_morning'></div></html> | <html><div id='monday_morning'></div></html> | ||

====Monday Morning (April 24th, 10:30am to 12:30pm)==== | ====Monday Morning (April 24th, 10:30am to 12:30pm)==== | ||

- | - Making Neural Programming Architectures Generalize via Recursion | + | C1: Making Neural Programming Architectures Generalize via Recursion\\ |

- | - Learning Graphical State Transitions | + | C2: Learning Graphical State Transitions\\ |

- | - Distributed Second-Order Optimization using Kronecker-Factored Approximations | + | C3: Distributed Second-Order Optimization using Kronecker-Factored Approximations\\ |

- | - Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes | + | C4: Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes\\ |

- | - Neural Program Lattices | + | C5: Neural Program Lattices\\ |

- | - Diet Networks: Thin Parameters for Fat Genomics | + | C6: Diet Networks: Thin Parameters for Fat Genomics\\ |

- | - Unsupervised Cross-Domain Image Generation | + | C7: Unsupervised Cross-Domain Image Generation\\ |

- | - Towards Principled Methods for Training Generative Adversarial Networks | + | C8: Towards Principled Methods for Training Generative Adversarial Networks\\ |

- | - Recurrent Mixture Density Network for Spatiotemporal Visual Attention | + | C9: Recurrent Mixture Density Network for Spatiotemporal Visual Attention\\ |

- | - Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer | + | C10: Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer\\ |

- | - Pruning Filters for Efficient ConvNets | + | C11: Pruning Filters for Efficient ConvNets\\ |

- | - Optimization as a Model for Few-Shot Learning | + | C12: Stick-Breaking Variational Autoencoders\\ |

- | - Understanding deep learning requires rethinking generalization | + | C13: Identity Matters in Deep Learning\\ |

- | - On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima | + | C14: On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima\\ |

- | - Recurrent Hidden Semi-Markov Model | + | C15: Recurrent Hidden Semi-Markov Model\\ |

- | - Nonparametric Neural Networks | + | C16: Nonparametric Neural Networks\\ |

- | - Learning to Generate Samples from Noise through Infusion Training | + | C17: Learning to Generate Samples from Noise through Infusion Training\\ |

- | - An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax | + | C18: An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax\\ |

- | - Highway and Residual Networks learn Unrolled Iterative Estimation | + | C19: Highway and Residual Networks learn Unrolled Iterative Estimation\\ |

- | - Soft Weight-Sharing for Neural Network Compression | + | C20: Soft Weight-Sharing for Neural Network Compression\\ |

- | - Snapshot Ensembles: Train 1, Get M for Free | + | C21: Snapshot Ensembles: Train 1, Get M for Free\\ |

- | - Towards a Neural Statistician | + | C22: Towards a Neural Statistician\\ |

- | - Learning Curve Prediction with Bayesian Neural Networks | + | C23: Learning Curve Prediction with Bayesian Neural Networks\\ |

- | - Learning End-to-End Goal-Oriented Dialog | + | C24: Learning End-to-End Goal-Oriented Dialog\\ |

- | - Multi-Agent Cooperation and the Emergence of (Natural) Language | + | C25: Multi-Agent Cooperation and the Emergence of (Natural) Language\\ |

- | - Efficient Vector Representation for Documents through Corruption | + | C26: Efficient Vector Representation for Documents through Corruption\\ |

- | - Improving Neural Language Models with a Continuous Cache | + | C27: Improving Neural Language Models with a Continuous Cache\\ |

- | - Program Synthesis for Character Level Language Modeling | + | C28: Program Synthesis for Character Level Language Modeling\\ |

- | - Tracking the World State with Recurrent Entity Networks | + | C29: Tracking the World State with Recurrent Entity Networks\\ |

- | - Reinforcement Learning with Unsupervised Auxiliary Tasks | + | C30: Reinforcement Learning with Unsupervised Auxiliary Tasks\\ |

- | - Neural Architecture Search with Reinforcement Learning | + | C31: Neural Architecture Search with Reinforcement Learning\\ |

- | - Sample Efficient Actor-Critic with Experience Replay | + | C32: Sample Efficient Actor-Critic with Experience Replay\\ |

- | - Learning to Act by Predicting the Future | + | C33: Learning to Act by Predicting the Future\\ |

<html><div id='monday_afternoon'></div></html> | <html><div id='monday_afternoon'></div></html> | ||

====Monday Afternoon (April 24th, 4:30pm to 6:30pm)==== | ====Monday Afternoon (April 24th, 4:30pm to 6:30pm)==== | ||

- | - Neuro-Symbolic Program Synthesis | + | C1: Neuro-Symbolic Program Synthesis\\ |

- | - Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy | + | C2: Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy\\ |

- | - Trained Ternary Quantization | + | C3: Trained Ternary Quantization\\ |

- | - DSD: Dense-Sparse-Dense Training for Deep Neural Networks | + | C4: DSD: Dense-Sparse-Dense Training for Deep Neural Networks\\ |

- | - A Compositional Object-Based Approach to Learning Physical Dynamics | + | C5: A Compositional Object-Based Approach to Learning Physical Dynamics\\ |

- | - Multilayer Recurrent Network Models of Primate Retinal Ganglion Cells | + | C6: Multilayer Recurrent Network Models of Primate Retinal Ganglion Cells\\ |

- | - Improving Generative Adversarial Networks with Denoising Feature Matching | + | C7: Improving Generative Adversarial Networks with Denoising Feature Matching\\ |

- | - Transfer of View-manifold Learning to Similarity Perception of Novel Objects | + | C8: Transfer of View-manifold Learning to Similarity Perception of Novel Objects\\ |

- | - What does it take to generate natural textures? | + | C9: What does it take to generate natural textures?\\ |

- | - Emergence of foveal image sampling from learning to attend in visual scenes | + | C10: Emergence of foveal image sampling from learning to attend in visual scenes\\ |

- | - PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications | + | C11: PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications\\ |

- | - Learning to Optimize | + | C12: Learning to Optimize\\ |

- | - Training Compressed Fully-Connected Networks with a Density-Diversity Penalty | + | C13: Do Deep Convolutional Nets Really Need to be Deep and Convolutional?\\ |

- | - Optimal Binary Autoencoding with Pairwise Correlations | + | C14: Optimal Binary Autoencoding with Pairwise Correlations\\ |

- | - On the Quantitative Analysis of Decoder-Based Generative Models | + | C15: On the Quantitative Analysis of Decoder-Based Generative Models\\ |

- | - Learning to Remember Rare Events | + | C16: Adversarial machine learning at scale\\ |

- | - Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks | + | C17: Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks\\ |

- | - Capacity and Learnability in Recurrent Neural Networks | + | C18: Capacity and Learnability in Recurrent Neural Networks\\ |

- | - Deep Learning with Dynamic Computation Graphs | + | C19: Deep Learning with Dynamic Computation Graphs\\ |

- | - Exploring Sparsity in Recurrent Neural Networks | + | C20: Exploring Sparsity in Recurrent Neural Networks\\ |

- | - Structured Attention Networks | + | C21: Structured Attention Networks\\ |

- | - Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations | + | C22: Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning\\ |

- | - Variational Lossy Autoencoder | + | C23: Variational Lossy Autoencoder\\ |

- | - Learning to Query, Reason, and Answer Questions On Ambiguous Texts | + | C24: Learning to Query, Reason, and Answer Questions On Ambiguous Texts\\ |

- | - Deep Biaffine Attention for Neural Dependency Parsing | + | C25: Deep Biaffine Attention for Neural Dependency Parsing\\ |

- | - A Compare-Aggregate Model for Matching Text Sequences | + | C26: A Compare-Aggregate Model for Matching Text Sequences\\ |

- | - Data Noising as Smoothing in Neural Network Language Models | + | C27: Data Noising as Smoothing in Neural Network Language Models\\ |

- | - Neural Variational Inference For Topic Models | + | C28: Neural Variational Inference For Topic Models\\ |

- | - Words or Characters? Fine-grained Gating for Reading Comprehension | + | C29: Bidirectional Attention Flow for Machine Comprehension\\ |

- | - Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic | + | C30: Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic\\ |

- | - Stochastic Neural Networks for Hierarchical Reinforcement Learning | + | C31: Stochastic Neural Networks for Hierarchical Reinforcement Learning\\ |

- | - Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning | + | C32: Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning\\ |

- | - Third Person Imitation Learning | + | C33: Third Person Imitation Learning\\ |

<html><div id='tuesday_morning'></div></html> | <html><div id='tuesday_morning'></div></html> | ||

====Tuesday Morning (April 25th, 10:30am to 12:30pm)==== | ====Tuesday Morning (April 25th, 10:30am to 12:30pm)==== | ||

- | - DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning | + | C1: DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning\\ |

- | - SampleRNN: An Unconditional End-to-End Neural Audio Generation Model | + | C2: A SELF-ATTENTIVE SENTENCE EMBEDDING\\ |

- | - Deep Probabilistic Programming | + | C3: Deep Probabilistic Programming\\ |

- | - Lie-Access Neural Turing Machines | + | C4: Lie-Access Neural Turing Machines\\ |

- | - Learning Features of Music From Scratch | + | C5: Learning Features of Music From Scratch\\ |

- | - Mode Regularized Generative Adversarial Networks | + | C6: Mode Regularized Generative Adversarial Networks\\ |

- | - End-to-end Optimized Image Compression | + | C7: End-to-end Optimized Image Compression\\ |

- | - Variational Recurrent Adversarial Deep Domain Adaptation | + | C8: Variational Recurrent Adversarial Deep Domain Adaptation\\ |

- | - Steerable CNNs | + | C9: Steerable CNNs\\ |

- | - Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning | + | C10: Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning\\ |

- | - PixelVAE: A Latent Variable Model for Natural Images | + | C11: PixelVAE: A Latent Variable Model for Natural Images\\ |

- | - A recurrent neural network without chaos | + | C12: A recurrent neural network without chaos\\ |

- | - Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer | + | C13: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer\\ |

- | - Tree-structured decoding with doubly-recurrent neural networks | + | C14: Tree-structured decoding with doubly-recurrent neural networks\\ |

- | - Introspection:Accelerating Neural Network Training By Learning Weight Evolution | + | C15: Introspection:Accelerating Neural Network Training By Learning Weight Evolution\\ |

- | - Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization | + | C16: Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization\\ |

- | - Quasi-Recurrent Neural Networks | + | C17: Quasi-Recurrent Neural Networks\\ |

- | - Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain | + | C18: Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain\\ |

- | - A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks | + | C19: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks\\ |

- | - Trusting SVM for Piecewise Linear CNNs | + | C20: Trusting SVM for Piecewise Linear CNNs\\ |

- | - Maximum Entropy Flow Networks | + | C21: Maximum Entropy Flow Networks\\ |

- | - The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables | + | C22: The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables\\ |

- | - Unrolled Generative Adversarial Networks | + | C23: Unrolled Generative Adversarial Networks\\ |

- | - A Simple but Tough-to-Beat Baseline for Sentence Embeddings | + | C24: A Simple but Tough-to-Beat Baseline for Sentence Embeddings\\ |

- | - Query-Reduction Networks for Question Answering | + | C25: Query-Reduction Networks for Question Answering\\ |

- | - Machine Comprehension Using Match-LSTM and Answer Pointer | + | C26: Machine Comprehension Using Match-LSTM and Answer Pointer\\ |

- | - Bidirectional Attention Flow for Machine Comprehension | + | C27: Words or Characters? Fine-grained Gating for Reading Comprehension\\ |

- | - Dynamic Coattention Networks For Question Answering | + | C28: Dynamic Coattention Networks For Question Answering\\ |

- | - Multi-view Recurrent Neural Acoustic Word Embeddings | + | C29: Multi-view Recurrent Neural Acoustic Word Embeddings\\ |

- | - Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement | + | C30: Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement\\ |

- | - Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning | + | C31: Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning\\ |

- | - Generalizing Skills with Semi-Supervised Reinforcement Learning | + | C32: Generalizing Skills with Semi-Supervised Reinforcement Learning\\ |

- | - Improving Policy Gradient by Exploring Under-appreciated Rewards | + | C33: Improving Policy Gradient by Exploring Under-appreciated Rewards\\ |

<html><div id='tuesday_afternoon'></div></html> | <html><div id='tuesday_afternoon'></div></html> | ||

- | ====Tuesday Afternoon (April 25th, 4:30pm to 6:30pm)==== | + | ====Tuesday Afternoon (April 25th, 2:00pm to 4:00pm)==== |

- | - Sigma Delta Quantized Networks | + | C1: Sigma Delta Quantized Networks\\ |

- | - Paleo: A Performance Model for Deep Neural Networks | + | C2: Paleo: A Performance Model for Deep Neural Networks\\ |

- | - DeepCoder: Learning to Write Programs | + | C3: DeepCoder: Learning to Write Programs\\ |

- | - Topology and Geometry of Deep Rectified Network Optimization Landscapes | + | C4: Topology and Geometry of Deep Rectified Network Optimization Landscapes\\ |

- | - Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights | + | C5: Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights\\ |

- | - Learning to Perform Physics Experiments via Deep Reinforcement Learning | + | C6: Learning to Perform Physics Experiments via Deep Reinforcement Learning\\ |

- | - Decomposing Motion and Content for Natural Video Sequence Prediction | + | C7: Decomposing Motion and Content for Natural Video Sequence Prediction\\ |

- | - Calibrating Energy-based Generative Adversarial Networks | + | C8: Calibrating Energy-based Generative Adversarial Networks\\ |

- | - Pruning Convolutional Neural Networks for Resource Efficient Inference | + | C9: Pruning Convolutional Neural Networks for Resource Efficient Inference\\ |

- | - Incorporating long-range consistency in CNN-based texture generation | + | C10: Incorporating long-range consistency in CNN-based texture generation\\ |

- | - Lossy Image Compression with Compressive Autoencoders | + | C11: Lossy Image Compression with Compressive Autoencoders\\ |

- | - LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation | + | C12: LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation\\ |

- | - Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data | + | C13: Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data\\ |

- | - Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data | + | C14: Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data\\ |

- | - Mollifying Networks | + | C15: Mollifying Networks\\ |

- | - beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework | + | C16: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework\\ |

- | - Categorical Reparameterization with Gumbel-Softmax | + | C17: Categorical Reparameterization with Gumbel-Softmax\\ |

- | - Online Bayesian Transfer Learning for Sequential Data Modeling | + | C18: Online Bayesian Transfer Learning for Sequential Data Modeling\\ |

- | - Latent Sequence Decompositions | + | C19: Latent Sequence Decompositions\\ |

- | - Density estimation using Real NVP | + | C20: Density estimation using Real NVP\\ |

- | - Recurrent Batch Normalization | + | C21: Recurrent Batch Normalization\\ |

- | - SGDR: Stochastic Gradient Descent with Restarts | + | C22: SGDR: Stochastic Gradient Descent with Restarts\\ |

- | - Variable Computation in Recurrent Neural Networks | + | C23: Variable Computation in Recurrent Neural Networks\\ |

- | - Deep Variational Information Bottleneck | + | C24: Deep Variational Information Bottleneck\\ |

- | - A SELF-ATTENTIVE SENTENCE EMBEDDING | + | C25: SampleRNN: An Unconditional End-to-End Neural Audio Generation Model\\ |

- | - TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency | + | C26: TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency\\ |

- | - Frustratingly Short Attention Spans in Neural Language Modeling | + | C27: Frustratingly Short Attention Spans in Neural Language Modeling\\ |

- | - Offline Bilingual Word Vectors Without a Dictionary | + | C28: Offline Bilingual Word Vectors, Orthogonal Transformations and the Inverted Softmax\\ |

- | - LEARNING A NATURAL LANGUAGE INTERFACE WITH NEURAL PROGRAMMER | + | C29: LEARNING A NATURAL LANGUAGE INTERFACE WITH NEURAL PROGRAMMER\\ |

- | - Designing Neural Network Architectures using Reinforcement Learning | + | C30: Designing Neural Network Architectures using Reinforcement Learning\\ |

- | - Metacontrol for Adaptive Imagination-Based Optimization | + | C31: Metacontrol for Adaptive Imagination-Based Optimization\\ |

- | - Recurrent Environment Simulators | + | C32: Recurrent Environment Simulators\\ |

- | - EPOpt: Learning Robust Neural Network Policies Using Model Ensembles | + | C33: EPOpt: Learning Robust Neural Network Policies Using Model Ensembles\\ |

<html><div id='wednesday_morning'></div></html> | <html><div id='wednesday_morning'></div></html> | ||

====Wednesday Morning (April 26th, 10:30am to 12:30pm)==== | ====Wednesday Morning (April 26th, 10:30am to 12:30pm)==== | ||

- | - Deep Multi-task Representation Learning: A Tensor Factorisation Approach | + | C1: Deep Multi-task Representation Learning: A Tensor Factorisation Approach\\ |

- | - Training deep neural-networks using a noise adaptation layer | + | C2: Training deep neural-networks using a noise adaptation layer\\ |

- | - Delving into Transferable Adversarial Examples and Black-box Attacks | + | C3: Delving into Transferable Adversarial Examples and Black-box Attacks\\ |

- | - Towards the Limit of Network Quantization | + | C4: Towards the Limit of Network Quantization\\ |

- | - Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music | + | C5: Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music\\ |

- | - Learning to superoptimize programs | + | C6: Learning to superoptimize programs\\ |

- | - Regularizing CNNs with Locally Constrained Decorrelations | + | C7: Regularizing CNNs with Locally Constrained Decorrelations\\ |

- | - Generative Multi-Adversarial Networks | + | C8: Generative Multi-Adversarial Networks\\ |

- | - Visualizing Deep Neural Network Decisions: Prediction Difference Analysis | + | C9: Visualizing Deep Neural Network Decisions: Prediction Difference Analysis\\ |

- | - FractalNet: Ultra-Deep Neural Networks without Residuals | + | C10: FractalNet: Ultra-Deep Neural Networks without Residuals\\ |

- | - Faster CNNs with Direct Sparse Convolutions and Guided Pruning | + | C11: Faster CNNs with Direct Sparse Convolutions and Guided Pruning\\ |

- | - FILTER SHAPING FOR CONVOLUTIONAL NEURAL NETWORKS | + | C12: FILTER SHAPING FOR CONVOLUTIONAL NEURAL NETWORKS\\ |

- | - The Neural Noisy Channel | + | C13: The Neural Noisy Channel\\ |

- | - Automatic Rule Extraction from Long Short Term Memory Networks | + | C14: Automatic Rule Extraction from Long Short Term Memory Networks\\ |

- | - Adversarially Learned Inference | + | C15: Adversarially Learned Inference\\ |

- | - Deep Information Propagation | + | C16: Deep Information Propagation\\ |

- | - Revisiting Classifier Two-Sample Tests | + | C17: Revisiting Classifier Two-Sample Tests\\ |

- | - Loss-aware Binarization of Deep Networks | + | C18: Loss-aware Binarization of Deep Networks\\ |

- | - Energy-based Generative Adversarial Networks | + | C19: Energy-based Generative Adversarial Networks\\ |

- | - Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning | + | C20: Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning\\ |

- | - Temporal Ensembling for Semi-Supervised Learning | + | C21: Temporal Ensembling for Semi-Supervised Learning\\ |

- | - On Detecting Adversarial Perturbations | + | C22: On Detecting Adversarial Perturbations\\ |

- | - Identity Matters in Deep Learning | + | C23: Understanding deep learning requires rethinking generalization\\ |

- | - Adversarial Feature Learning | + | C24: Adversarial Feature Learning\\ |

- | - Learning through Dialogue Interactions | + | C25: Learning through Dialogue Interactions\\ |

- | - Learning to Compose Words into Sentences with Reinforcement Learning | + | C26: Learning to Compose Words into Sentences with Reinforcement Learning\\ |

- | - Batch Policy Gradient Methods for Improving Neural Conversation Models | + | C27: Batch Policy Gradient Methods for Improving Neural Conversation Models\\ |

- | - Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling | + | C28: Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling\\ |

- | - Geometry of Polysemy | + | C29: Geometry of Polysemy\\ |

- | - PGQ: Combining policy gradient and Q-learning | + | C30: PGQ: Combining policy gradient and Q-learning\\ |

- | - Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU | + | C31: Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU\\ |

- | - Learning to Navigate in Complex Environments | + | C32: Learning to Navigate in Complex Environments\\ |

- | - Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks | + | C33: Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks\\ |

<html><div id='wednesday_afternoon'></div></html> | <html><div id='wednesday_afternoon'></div></html> | ||

====Wednesday Afternoon (April 26th, 4:30pm to 6:30pm)==== | ====Wednesday Afternoon (April 26th, 4:30pm to 6:30pm)==== | ||

- | - Learning recurrent representations for hierarchical behavior modeling | + | C1: Learning recurrent representations for hierarchical behavior modeling\\ |

- | - Predicting Medications from Diagnostic Codes with Recurrent Neural Networks | + | C2: Predicting Medications from Diagnostic Codes with Recurrent Neural Networks\\ |

- | - Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks | + | C3: Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks\\ |

- | - HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving | + | C4: HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving\\ |

- | - Learning Invariant Representations Of Planar Curves | + | C5: Learning Invariant Representations Of Planar Curves\\ |

- | - Entropy-SGD: Biasing Gradient Descent Into Wide Valleys | + | C6: Entropy-SGD: Biasing Gradient Descent Into Wide Valleys\\ |

- | - Amortised MAP Inference for Image Super-resolution | + | C7: Amortised MAP Inference for Image Super-resolution\\ |

- | - Inductive Bias of Deep Convolutional Networks through Pooling Geometry | + | C8: Inductive Bias of Deep Convolutional Networks through Pooling Geometry\\ |

- | - Neural Photo Editing with Introspective Adversarial Networks | + | C9: Neural Photo Editing with Introspective Adversarial Networks\\ |

- | - A Learned Representation For Artistic Style | + | C10: A Learned Representation For Artistic Style\\ |

- | - Adversarial Machine Learning at Scale | + | C11: Learning to Remember Rare Events\\ |

- | - Stick-Breaking Variational Autoencoders | + | C12: Optimization as a Model for Few-Shot Learning\\ |

- | - Support Regularized Sparse Coding and Its Fast Encoder | + | C13: Support Regularized Sparse Coding and Its Fast Encoder\\ |

- | - Discrete Variational Autoencoders | + | C14: Discrete Variational Autoencoders\\ |

- | - Do Deep Convolutional Nets Really Need to be Deep and Convolutional? | + | C15: Training Compressed Fully-Connected Networks with a Density-Diversity Penalty\\ |

- | - Efficient Representation of Low-Dimensional Manifolds using Deep Networks | + | C16: Efficient Representation of Low-Dimensional Manifolds using Deep Networks\\ |

- | - Semi-Supervised Classification with Graph Convolutional Networks | + | C17: Semi-Supervised Classification with Graph Convolutional Networks\\ |

- | - Understanding Neural Sparse Coding with Matrix Factorization | + | C18: Understanding Neural Sparse Coding with Matrix Factorization\\ |

- | - Tighter bounds lead to improved classifiers | + | C19: Tighter bounds lead to improved classifiers\\ |

- | - Why Deep Neural Networks for Function Approximation? | + | C20: Why Deep Neural Networks for Function Approximation?\\ |

- | - Hierarchical Multiscale Recurrent Neural Networks | + | C21: Hierarchical Multiscale Recurrent Neural Networks\\ |

- | - Dropout with Expectation-linear Regularization | + | C22: Dropout with Expectation-linear Regularization\\ |

- | - HyperNetworks | + | C23: HyperNetworks\\ |

- | - Hadamard Product for Low-rank Bilinear Pooling | + | C24: Hadamard Product for Low-rank Bilinear Pooling\\ |

- | - Adversarial Training Methods for Semi-Supervised Text Classification | + | C25: Adversarial Training Methods for Semi-Supervised Text Classification\\ |

- | - Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks | + | C26: Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks\\ |

- | - Pointer Sentinel Mixture Models | + | C27: Pointer Sentinel Mixture Models\\ |

- | - Reasoning with Memory Augmented Neural Networks for Language Comprehension | + | C28: Reasoning with Memory Augmented Neural Networks for Language Comprehension\\ |

- | - Dialogue Learning With Human-in-the-Loop | + | C29: Dialogue Learning With Human-in-the-Loop\\ |

- | - Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning | + | C30: Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations\\ |

- | - Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening | + | C31: Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening\\ |

- | - Learning Visual Servoing with Deep Features and Trust Region Fitted Q-Iteration | + | C32: Learning Visual Servoing with Deep Features and Trust Region Fitted Q-Iteration\\ |

- | - An Actor-Critic Algorithm for Sequence Prediction | + | C33: An Actor-Critic Algorithm for Sequence Prediction\\ |