Dr. Reio presented several papers at an AI meeting that explored topics including grounding topic models with knowledge bases, a survey of Bayesian deep learning, using recurrent neural networks for visual paragraph generation based on long-range semantic dependencies, and examining natural language understanding unit tests and semantic representations.

1. Dr.Reio's papers at AI meeting
• Grounding Topic Models with Knowledge Bases
https://www.microsoft.com/en-us/research/wp-
content/uploads/2016/12/ijcai16ground.pdf
• Supplementary Material
https://www.cs.cmu.edu/~zhitingh/data/ijcai16ground_supp.pdf
• Towards Bayesian Deep Learning: A Survey
https://arxiv.org/abs/1604.01662
• Recurrent Topic-Transition GAN for Visual Paragraph Generation
https://arxiv.org/pdf/1703.07022.pdf
• TopicRNN: A Recurrent Neural Network with Long-Range Semantic
Dependency
https://arxiv.org/abs/1611.01702
• 自然言語理解ユニットテストと意味表現の検討
https://kaigi.org/jsai/webprogram/2016/pdf/1086.pdf