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.
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]
Talk at INA-18
Yuta Suzuki, a master’s course student, presented about applying machine learning to neurotoxicity assessment at the International Neurotoxicology Association Conference (INA-18). This work is joint research with National Institute of Health Sciences.
[Read More]New members!
Members’ Page has been updated.
Accepted to GECCO 2023
Our paper has been accepted to Genetic and Evolutionary Computation Conference (GECCO) 2023. This paper introduces the margin correction method into (1+1)-CMA-ES and evaluates it on discrete and mixed-integer black-box optimization problems. This paper is joint work with M. Nomura at CyberAgent, Inc.
[Read More]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.
[Read More]Selected as Top Reviewers at NeurIPS 2022
Prof. Shirakawa has been selected as a Top Reviewer at NeurIPS 2022. [Link]