Invited Speakers
Important Dates
Supported by
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Additional cooperation from
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Third International Workshop on Symbolic-Neural Learning (SNL-2019)

July 11-12, 2019
Miraikan hall, Odaiba Miraikan 7F (Tokyo, Japan)

Keynote Speakers:

Maximilian Nickel Facebook Representation Learning in Symbolic Domains
Noah Smith University of Washington/Allen Institute for Artificial Intelligence Rational Recurrences for Empirical Natural Language Processing
Kristina Toutanova Google Learning and evaluating generalizable vector space representations of texts

Invited Speakers:

Chenhui Chu Osaka University Visually Grounded Paraphrase Identification
Ryutaro Ichise National Institute of Informatics/AIST AIRC Knowledge Graph Construction
Masaaki Imaizumi Institute of Statistical Mathematics Generalization Analysis for Mechanism of Deep Neural Networks via Nonparametric Statistics
David McAllester Toyota Technological Institute at Chicago Rate-Distortion Autoencoding for Unsupervised Machine Translation
Hirohiko Niioka Osaka University Bio-Medical imaging supported by deep learning
Takahiro Shinozaki Tokyo Institute of Technology Automated Development of Deep Neural Network Systems Based on Evolutionary Algorithms
Karl Stratos Toyota Technological Institute at Chicago Mutual Information Maximization for Simple and Effective Label Induction in Text
Jun Suzuki Tohoku University/RIKEN AIP Usability Enhancements to Neural Word Embeddings
Taiji Suzuki The University of Tokyo/RIKEN AIP Adaptivity of deep learning in Besov space with its connection to sparse estimation
Masashi Tsubaki AIST AIRC Deep learning for graph-structured data: applications to drug and materials discovery
Yoshimasa Tsuruoka The University of Tokyo/AIST AIRC Combining Knowledge and Deep Reinforcement Learning for Plant Control
Norimichi Ukita Toyota Technological Institute Image and Video Super-resolution for Learning Visual Representations: Application to Tiny Object Detection
Matthew Walter Toyota Technological Institute at Chicago Joint Optimization over Robot Motion and Control