Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA). - Kenji Nagata; Yoh-ichi Mototake; Rei Muraoka; Takehiko Sasaki; Masato Okada (CoRR, 2018)

Semi-flat minima and saddle points by embedding neural networks to overparameterization. - Kenji Fukumizu; Shoichiro Yamaguchi; Yoh-ichi Mototake; Mirai Tanaka (NeurIPS, 2019)

Revealing the existence of the ontological commitment in fish schools. - Takayuki Niizato; Kotaro Sakamoto; Yoh-ichi Mototake; Hisashi Murakami; Yuta Nishiyama; Toshiki Fukushima (Artif. Life Robotics, 2020)

Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks. - Yoh-ichi Mototake (CoRR, 2020)

Four-Types of IIT-Induced Group Integrity of Plecoglossus altivelis. - Takayuki Niizato; Kotaro Sakamoto; Yoh-ichi Mototake; Hisashi Murakami; Takenori Tomaru; Tomotaro Hoshika; Toshiki Fukushima (Entropy, 2020)

Comparison of neuronal responses in primate inferior-temporal cortex and feed-forward deep neural network model with regard to information processing of faces. - Narihisa Matsumoto; Yoh-ichi Mototake; Kenji Kawano; Masato Okada; Yasuko Sugase-Miyamoto (J. Comput. Neurosci., 2021)

Forecasting of the development of a partially-observed dynamical time series with the aid of time-invariance and linearity. - Akifumi Okuno; Yuya Morishita; Yoh-ichi Mototake (CoRR, 2023)

Autoregressive With Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series. - Akifumi Okuno; Yuya Morishita; Yoh-ichi Mototake (IEEE Access, 2024)