Our paper has been accepted to Knowledge-Based Systems. This paper proposes a generation method of EPMA images with desirable physical characteristics based on deep learning. This work is collaborative research with Toyota Motor Corporation.

• Kento Uchida, Genki Sakata, Tetsushi Watari, Yuta Yamakita, and Shinichi Shirakawa: Generation of microscopic structure of solder material with desirable characteristics based on deep learning, Knowledge-Based Systems, Vol. 258, 110017, Dec. 2022. [DOI]

Our paper has been accepted to the Workshop on Edge Learning over 5G Mobile Networks and Beyond in IEEE GLOBECOM 2022). This work is collaborative research with Prof. Nishio at Tokyo Institute of Technology. This paper proposes a neural architecture search method for split computing in which neural networks are split and located at edge devices and servers for inference.

Our paper has been accepted to Ocean Engineering. This paper proposes a method for obtaining online berthing control law using supervised learning and reinforcement learning. This work is collaborative research with the group of Prof. Maki at Osaka University.

• Shoma Shimizu, Kenta Nishihara, Yoshiki Miyauchi, Kouki Wakita, Rin Suyama, Atsuo Maki, and Shinichi Shirakawa: Automatic berthing using supervised learning and reinforcement learning, Ocean Engineering, Vol. 265, 112553, Dec. 2022. [DOI]

Our paper has been accepted to the IEEE Symposium Series On Computational Intelligence (SSCI 2022). This paper proposes an improved method of Separable CMA-ES for objective functions with low effective dimensionality.

• Teppei Yamaguchi, Kento Uchida, and Shinichi Shirakawa: Improvement of sep-CMA-ES for Optimization of High-Dimensional Functions with Low Effective Dimensionality, 2022 IEEE Symposium Series On Computational Intelligence, Singapore, December 4-7, 2022 (Accepted).

Prof. Shirakawa has been selected as an Outstanding Reviewer (Top 10%) at ICML 2022. [Link]

Our paper has been accepted to the 31st International Conference on Artificial Neural Networks (ICANN 2022). This paper proposes an efficient search method for multiple neural architectures with different complexities by using importance sampling.

• Yuhei Noda, Shota Saito, and Shinichi Shirakawa: Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling, 31st International Conference on Artificial Neural Networks (ICANN 2022), September 6-9 2022 (Accepted). [arXiv]

Our papers (two full papers, one poster paper, and one workshop paper) have been accepted to the Genetic and Evolutionary Computation Conference (GECCO) 2022.

The members’ page has been updated. Now, our laboratory has 5 doctoral course students, 14 master’s course students, and 6 undergraduate students for graduation research.

[Members page]

Our paper regarding text-to-gesture generation based on deep learning has been accepted to Neural Networks. This work is based on the master’s course research by Asakawa and is collaborative research with Prof. Hasegawa at Hokkai Gakuen University and Prof. Kaneko at Aoyama Gakuin University.

Eiichi Asakawa, Naoshi Kaneko, Dai Hasegawa, and Shinichi Shirakawa: Evaluation of text-to-gesture generation model using convolutional neural network, Neural Networks, Elsevier [Link]

The following paper has been published in Scientific Reports. This work is the collaborative research with Prof. Fukuda and Prof. Ohmori at Yokohama National University.

Minami Masumoto, Ittetsu Fukuda, Suguru Furihata, Takahiro Arai, Tatsuto Kageyama, Kiyomi Ohmori, Shinichi Shirakawa, and Junji Fukuda: Deep neural network for the determination of transformed foci in Bhas 42 cell transformation assay, Scientific Reports, volume 11, Article number: 23344, Dec. 2021. [Link]