# Differences

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iclr2017:conference_posters [2017/04/05 17:11] hugo |
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> | ||

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

C12: Stick-Breaking Variational Autoencoders\\ | C12: Stick-Breaking Variational Autoencoders\\ | ||

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

C14: 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\\ | ||

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

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C11: 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\\ | ||

C12: Learning to Optimize\\ | C12: Learning to Optimize\\ | ||

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

C24: Adversarial Feature Learning\\ | C24: Adversarial Feature Learning\\ | ||

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

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

C14: Discrete Variational Autoencoders\\ | C14: Discrete Variational Autoencoders\\ | ||

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

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

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