松尾 豊：深層学習と人工知能, 認知科学, Vol.28, No.2, 2021

松尾 豊：人工知能関連技術の歴史と技術動向, 電子情報通信学会誌, Vol.103, No.5, 2020

松尾 豊：人工知能の現状・今後とハードウェアへの期待，応用物理（応用物理学会誌），2019

岩澤 有祐，矢入 郁子，松尾 豊：深層学習の進歩とインタフェース，ヒューマンインタフェース学会論文誌，2017

松尾 豊：サービスに対するディープラーニングの可能性，サービソロジー，Vol.4, No.1, pp.10-15, 2017

松尾 豊：人工知能から見たロボット界への期待, 日本ロボット学会誌, Vol.35, No.3, pp.174-179, 2017

松尾 豊：人工知能と倫理, 情報処理学会誌, Vol.57, No.10, pp.985-987, 2016

松尾 豊, 西田 豊明, 堀 浩一, 武田英明, 長谷敏司, 塩野 誠, 服部 宏充, 江間 有沙, 長倉 克枝：人工知能と倫理, 人工知能学会誌, Vol.31, No.5, pp.635-641, 2016

松尾 豊：ディープラーニング活用の可能性, 建築雑誌（日本建築学会誌）, Vol.131, No.1685, pp.20-21, 2016

松尾 豊：ディープラーニングと人工知能の難問, システム制御情報学会誌, Vol.60, No.3, pp.92-98, 2016

Hiroki Furuta, Yutaka Matsuo, and Shixiang Shane Gu. “Generalized Decision Transformer for Offline Hindsight Infomation Matching”, International Conference on Learning Representations 2022 (ICLR2022, Spotlight). April 2022.

Shizuma Kazutoshi Shinoda, Yuki Takezawa, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo, Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following , Workshop on Novel Ideas in Learning-to-Learn through Interaction, EMNLP 2021, 2021.

Hiroki Furuta, Yutaka Matsuo, and Shixiang Shane Gu. “Generalized Decision Transformer for Offline Hindsight Infomation Matching”, Deep Reinforcement Learning Workshop in Neural Information Processing Systems 2021 (NeurIPS2021). December 2021.

Yusuke Iwasawa, and Yutaka Matsuo. “Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization”, Advances in Neural Information Processing Systems 2021 (NeurIPS2021, Spotlight). December 2021.

Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, and Shixiang Shane Gu. “Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning”, Advances in Neural Information Processing Systems 2021 (NeurIPS2021). December 2021.

Machel Reid, Junjie Hu, Graham Neubig and Yutaka Matsuo. “AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages”, The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021). November 2021. Association for Computational Linguistics.

Machel Reid, Edison Marrese-Taylor and Yutaka Matsuo. “Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers”, Findings of The 2021 Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP 2021). November 2021. Association for Computational Linguistics.

Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, and Shixiang Shane Gu. “Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning”, International Conference on Machine Learning 2021 (ICML2021). July 2021.

Machel Reid and Victor Zhong. “LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer”, Findings of the Association for Computational Linguistics: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021). August 2021.

Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, and Shixiang Shane Gu. “Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning”, NERL 2021 Workshop on A Roadmap to Never-Ending Reinforcement Learning at International Conference on Learning Representations 2021 (ICLR2021) (Contributed Talk). May 2021.