Research
研究
研究業績
カテゴリー
研究領域
年
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Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, and Shixiang Shane Gu.
Advances in Neural Information Processing Systems 2021 (NeurIPS2021). December 2021.
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Information-theoretic regularization for learning global features by sequential VAE
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Mach Learn (2021)
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深層ニューラルネットワークによるクラスと幾何変換の同時分類確率を利用した分布外検知
岡本弘野, 鈴木雅大, 松尾豊
情報処理学会論文誌, Vol.62, No.7, pp.1382-1392 (2021)
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深層ニューラルネットワークの中間層出力を利用した半教師あり分布外検知
岡本弘野, 鈴木雅大, 松尾豊
情報処理学会論文誌, Vol.62, No.4, pp.1142-1151 (2021)
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Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, and Shixiang Shane Gu
International Conference on Machine Learning 2021 (ICML2021).
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Identifying Co-Adaptation of Algorithmic and implementational Innovations in Deep Reinforcement Learning: Taxonomy of Inference-based Algorithms
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Shane Gu.
International Conference on Machine Learning 2021 (ICML2021).
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Reward and Optimality Empowerments: Information-Theoretic Measures for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, and Shixiang Shane Gu.
International Conference on Machine Learning 2021 (ICML2021). July 2021. [paper]
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Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, and Yutaka Matsuo.
International Conference on Learning Representations 2021 (ICLR2021).
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第34回人工知能学会全国大会 学生奨励賞, “償却推論にもとづいた継続学習”
川島 寛乃，河野 慎，熊谷 亘, 松井 孝太，中澤 仁
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深層生成モデルを用いた半教師ありマルチモーダル学習
鈴木雅大, 松尾豊
情報処理学会論文誌, Vol. 59, No. 12 (2018)