New members!

Members’ Page has been updated. Now, our laboratory has 8 doctoral course students, 14 master’s course students, and 6 undergraduate students for graduation research.

Accepted to Knowledge-Based Systems

Our paper regarding the conversion of tabular data to image data has been accepted to Knowledge-Based Systems.

  • Takuya Matsuda, Kento Uchida, Shota Saito, and Shinichi Shirakawa: HACNet: End-to-end learning of interpretable table-to-image converter and convolutional neural network, Knowledge-Based Systems, Vol. 284, 111293, Jan. 2024. [DOI]

Accepted to ACM Transactions on Evolutionary Learning and Optimization

Our paper regarding the CMA-ES with Margin for mixed-integer black-box optimization problems has been accepted to ACM Transactions on Evolutionary Learning and Optimization. This paper is joint work with M. Nomura at CyberAgent, Inc.

  • Ryoki Hamano, Shota Saito, Masahiro Nomura, and Shinichi Shirakawa: Marginal Probability-Based Integer Handling for CMA-ES Tackling Single-and Multi-Objective Mixed-Integer Black-Box Optimization, ACM Transactions on Evolutionary Learning and Optimization. [DOI] [arXiv]

Accepted to Neural Networks Journal

Our paper regarding the automatic termination for neural architecture search has been accepted to Neural Networks. This work is collaborative research with Prof. Hino (Institute of Statistical Mathematics), etc.

  • Kotaro Sakamoto, Hideaki Ishibashi, Rei Sato, Shinichi Shirakawa, Youhei Akimoto, and Hideitsu Hino: ATNAS: Automatic Termination for Neural Architecture Search, Neural Networks, Vol. 166, pp. 446-458, Sep. 2023. [DOI]

Accepted to EvoApplications 2023 (Part of Evostar 2023)

Our paper has been accepted to EvoApplications 2023 (Part of Evostar 2023). This work proposes a switching mechanism for target variables of the surrogate model in surrogate-assisted (1+1)-CMA-ES.

  • Yutaro Yamada, Kento Uchida, Shota Saito, and Shinichi Shirakawa: Surrogate-Assisted (1+1)-CMA-ES with Switching Mechanism of Utility Functions, EvoApplications 2023 (Held as Part of EvoStar 2023), Brno, Czech Republic, April 12-14, 2023.

Accepted to HUCAPP 2023

Our paper has been accepted to HUCAPP 2023. This work is collaborative research with Prof. Hasegawa at Hokkai Gakuen University and Prof. Kaneko at Aoyama Gakuin University. This paper proposes a method for applying a text-to-gesture generation model trained using an English dataset to Japanese speakers’ data.

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