# Differences

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iclr2017:workshop_posters [2017/03/29 09:23] hugo |
iclr2017:workshop_posters [2017/04/23 09:27] (current) hugo |
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Below are the Workshop 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 Workshop Track. | Below are the Workshop 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 Workshop 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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W6: Accelerating Eulerian Fluid Simulation With Convolutional Networks\\ | W6: Accelerating Eulerian Fluid Simulation With Convolutional Networks\\ | ||

W7: Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels\\ | W7: Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels\\ | ||

- | W8: Deep Nets Don't Learn via Memorization\\ | + | W8: Dataset Augmentation in Feature Space\\ |

W9: Learning Algorithms for Active Learning\\ | W9: Learning Algorithms for Active Learning\\ | ||

W10: Reinterpreting Importance-Weighted Autoencoders\\ | W10: Reinterpreting Importance-Weighted Autoencoders\\ | ||

W11: Robustness to Adversarial Examples through an Ensemble of Specialists\\ | W11: Robustness to Adversarial Examples through an Ensemble of Specialists\\ | ||

- | W12: Neural Expectation Maximization\\ | + | W12: (empty) \\ |

W13: On Hyperparameter Optimization in Learning Systems\\ | W13: On Hyperparameter Optimization in Learning Systems\\ | ||

W14: Recurrent Normalization Propagation\\ | W14: Recurrent Normalization Propagation\\ | ||

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W17: Joint Embeddings of Scene Graphs and Images\\ | W17: Joint Embeddings of Scene Graphs and Images\\ | ||

W18: Unseen Style Transfer Based on a Conditional Fast Style Transfer Network\\ | W18: Unseen Style Transfer Based on a Conditional Fast Style Transfer Network\\ | ||

+ | |||

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

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W17: Adversarial Discriminative Domain Adaptation (workshop extended abstract)\\ | W17: Adversarial Discriminative Domain Adaptation (workshop extended abstract)\\ | ||

W18: Efficient Sparse-Winograd Convolutional Neural Networks\\ | W18: Efficient Sparse-Winograd Convolutional Neural Networks\\ | ||

+ | W19: Neural Expectation Maximization\\ | ||

+ | |||

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

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<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)==== |

W1: Lifelong Perceptual Programming By Example\\ | W1: Lifelong Perceptual Programming By Example\\ | ||

W2: Neu0\\ | W2: Neu0\\ | ||

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W15: Compositional Kernel Machines\\ | W15: Compositional Kernel Machines\\ | ||

W16: Loss is its own Reward: Self-Supervision for Reinforcement Learning\\ | W16: Loss is its own Reward: Self-Supervision for Reinforcement Learning\\ | ||

- | W17: Changing Model Behavior at Test-time Using Reinforcement Learning\\ | + | W17: REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models\\ |

W18: Precise Recovery of Latent Vectors from Generative Adversarial Networks\\ | W18: Precise Recovery of Latent Vectors from Generative Adversarial Networks\\ | ||

W19: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization\\ | W19: Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization\\ | ||

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W9: Trace Norm Regularised Deep Multi-Task Learning\\ | W9: Trace Norm Regularised Deep Multi-Task Learning\\ | ||

W10: Deep Learning with Sets and Point Clouds\\ | W10: Deep Learning with Sets and Point Clouds\\ | ||

- | W11: Dataset Augmentation in Feature Space\\ | + | W11: Deep Nets Don't Learn via Memorization\\ |

W12: Multiplicative LSTM for sequence modelling\\ | W12: Multiplicative LSTM for sequence modelling\\ | ||

W13: Learning to Discover Sparse Graphical Models\\ | W13: Learning to Discover Sparse Graphical Models\\ | ||

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W10: Compact Embedding of Binary-coded Inputs and Outputs using Bloom Filters\\ | W10: Compact Embedding of Binary-coded Inputs and Outputs using Bloom Filters\\ | ||

W11: Semi-supervised deep learning by metric embedding\\ | W11: Semi-supervised deep learning by metric embedding\\ | ||

- | W12: REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models\\ | + | W12: Changing Model Behavior at Test-time Using Reinforcement Learning\\ |

W13: Variational Reference Priors\\ | W13: Variational Reference Priors\\ | ||

W14: Gated Multimodal Units for Information Fusion\\ | W14: Gated Multimodal Units for Information Fusion\\ |