Yoh-ichi Mototake

According to our database, Yoh-ichi Mototake authored at least 10 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Unsupervised feature selection using Bayesian Tucker decomposition.

Y-h. Taguchi, Yoh-ichi Mototake

CoRR, April, 2026

2025

Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI.

Yoh-ichi Mototake, Makoto Sasaki

CoRR, November, 2025

2024

Autoregressive With Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series.

Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake

IEEE Access, 2024

2023

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

2021

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

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

Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks.

Yoh-ichi Mototake

CoRR, 2020

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

2019

Semi-flat minima and saddle points by embedding neural networks to overparameterization.

Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

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