The program is now available for the conference on Symbolic-Neural Learning <http://www.ttic.edu/SNL2017/> (SNL) to be held in Nagoya Japan July 7-8.
Registration deadline: Friday, June 9, 2017 Program: First International Workshop on Symbolic-Neural Learning (July 7-8) Nagoya Congress Center, Japan (Friday, July 7, 2017) 13:00-13:10 Opening 13:10-14:10 Keynote talk I (invited): Yoshua Bengio 14:10-14:30 Coffee break 14:30-15:30 Keynote talk II: Masashi Sugiyama 15:30-15:40 Break 15:40-17:20 Session 1: Computer Vision Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto and Masashi Sugiyama, Learning Discrete Representations via Information Maximizing Self-Augmented Training Ruotian Luo and Gregory Shakhnarovich, Comprehension-guided referring expressions Tristan Hascoet, Yasuo Ariki and Tetsuya Takiguchi, Semantic Web and Zero-Shot Learning of Large Scale Visual Classes. Bowen Shi, Taehwan Kim, Jonathan Keane, Weiran Wang, Hao Tang, Gregory Shakhnarovich, Diane Brentari and Karen Livescu, Neural Models for Fingerspelling Recognition from Video 18:00-20:00 Banquet (Saturday, July 8, 2017) 9:00-10:00 Keynote talk III (invited): William Cohen 10:00-10:20 Coffee break 10:20-12:00 Session 2: Speech & Language Andrea F. Daniele, Mohit Bansal and Matthew Walter, Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation Kurt Junshean Espinosa, Riza Batista-Navarro and Sophia Ananiadou, 5Ws: What Went Wrong with Word embeddings Shubham Toshniwal, Hao Tang, Liang Lu and Karen Livescu, Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition ohn Wieting, Mohit Bansal, Kevin Gimpel and Karen Livescu, Neural Architectures for Modeling Compositionality in Natural Language 12:00-13:30 Lunch break 13:30-14:30 Keynote talk IV: Jun-ichi Tsujii 14:30-15:30 Poster Session Matthew Holland and Kazushi Ikeda, Variance reduction using biased gradient estimates Zhenghang Cui, Issei Sato and Masashi Sugiyama, Stochastic Divergence Minimization for Biterm Topic Model Yin Jun Phua, Sophie Tourret and Katsumi Inoue, Learning Logic Program Representation from Delayed Interpretation Transition Using Recurrent Neural Networks Haiping Huang, Alireza Goudarzi and Taro Toyoizumi, Combining DropConnect and Feedback Alignment for Efficient Regularization in Deep Networks Jen-Tzung Chien and Ching-Wei Huang, Variational and Adversarial Domain Adaptation Miki Ueno, Comic Book Interpretation based on Deep Neural Networks Nicholas Altieri, Sherdil Niyaz, Samee Ibraheem and John Denero, Improved Word and Symbol Embedding for Part-of-Speech Tagging Marc Evrard, Makoto Miwa and Yutaka Sasaki, TTI's Approaches to Symbolic-Neural Learning* 15:30-17:35 Session 3: New Learning Approach Tomoya Sakai, Marthinus Christoffel Du Plessis, Gang Niu and Masashi Sugiyama, Semi-Supervised Classification based on Positive-Unlabeled Classification Han Bao, Masashi Sugiyama and Issei Sato, Multiple Instance Learning from Positive and Unlabeled Data Tetsuya Hada, Akifumi Okuno and Hidetoshi Shimodaira, Deep Multi-view Representation Learning Based on Adaptive Weighted Similarity Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor and Samuel Kaski, Localized Lasso for High-Dimensional Regression Tim Rocktaschel and Sebastian Riedel, End-to-end Differentiable Proving 17:40-17:50 Closing ---------------------------------------------------------
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