Research
研究
研究業績
カテゴリー
研究領域
年
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End-to-end Training of Deep Boltzmann Machines by Unbiased Contrastive Divergence with Local Mode Initialization
Shohei Taniguchi, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo
International Conference on Machine Learning (ICML 2023) July 2023.
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Interaction-Based Disentanglement of Entities for Object-Centric World Models
Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo.
“InteractioInternational Conference on Learning Representations (ICLR2023)
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Langevin Autoencoders for Learning Deep Latent Variable Models
Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai, Yutaka Matsuo
Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
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A survey of multimodal deep generative models
Masahiro Suzuki, Yutaka Matsuo
Advanced Robotics.(2022)
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複雑な環境における階層再帰型状態空間モデルの学習
原田憲旺, 鈴木雅大, 松尾豊
人工知能学会全国大会2021
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Transformerを用いた深層生成モデルによる教師なし物体認識手法の提案
小林由弥, 鈴木雅大, 松尾豊
人工知能学会全国大会2021
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Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-Reinforcement Learning
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Learning for Dynamics and Control (L4DC)
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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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深層生成モデルと世界モデル
第13回汎用人工知能研究会
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Pixyz: a framework for developing deep generative models
Tutorial on Deep Probabilistic Generative Models for Robotics (IROS2020)