Norikazu Takahashi

Professor, Doctor of Engineering
Information and Mathematical Engineering Laboratory, Faculty of Environmental, Life, Natural Science and Technology, Okayama University

Recent Works

• Han Xiao, Tsuyoshi Migita and Norikazu Takahashi, “A new Boolean matrix factorization algorithm based on Cardano’s method,” presented at 2024 the 7th International Conference on Information and Computer Technologies, March 15-17, 2024.

• Tsuyoshi Migita and Norikazu Takahashi, “Estimation of neural field texture and scene parameters using differentiable raytracing,” Proceedings of the 30th International Workshop on Frontiers of Computer Vision, February 19-24, 2024.

• Takao Masuda, Kento Yamada, Tsuyoshi Migita and Norikazu Takahashi, “A novel HALS-based iterative algorithm for randomized nonnegative matrix factorization,” Proceedings of the 2023 International Symposium on Nonlinear Theory and Its Applications, pp.704-707, Sepember, 2023.

• Norikazu Takahashi, “Zangwill's global convergence theorem and its application to nonnegative matrix factorization algorithms,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Science (Japanese Edition), vol.J106-A, no.9, pp.242-254, September 2023 (in Japanese).

• Shohei Kan, Norikazu Takahashi, Masaki Himuro, Akito Mashino, Kengo Iokibe and Yoshitaka Toyota, “Multi-objective design of filter installed in brush motor by artificial neural network accounting for cable length,” Proceedings of the 2023 IEEE International Symposium on Electromagnetic Compatibility, Signal & Power Integrity (EMC+SIPI2023), p.591, July-August 2023.

• Daiki Hirata and Norikazu Takahashi, “Ensemble learning in CNN augmented with fully connected subnetworks,” IEICE Transactions on Information and Systems, vol.E106-D, no.7, pp.1258-1261, July 2023.

• Norikazu Takahashi, Tsuyoshi Yamakawa, Yasuhiro Minetoma, Tetsuo Nishi and Tsuyoshi Migita, “Design of continuous-time recurrent neural networks with piecewise-linear activation function for generation of prescribed sequences of bipolar vectors,” Neural Networks, vol.164, pp.588-605, July 2023.