no code implementations • 11 Dec 2018 • Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka, Takehiko Sasaki, Masato Okada
Since the measurement time is strongly related to the signal-to-noise ratio for the Poisson noise model, Bayesian measurement with Poisson noise model enables us to clarify the relationship between the measurement time and the limit of estimation.
no code implementations • NeurIPS 2019 • Kenji Fukumizu, Shoichiro Yamaguchi, Yoh-ichi Mototake, Mirai Tanaka
We theoretically study the landscape of the training error for neural networks in overparameterized cases.
no code implementations • 31 Dec 2019 • Yoh-ichi Mototake
We propose a novel framework that can infer the hidden conservation laws of a complex system from deep neural networks (DNNs) that have been trained with physical data of the system.
no code implementations • 13 Apr 2023 • Yoh-ichi Mototake, Y-h. Taguchi
We propose that the correlation length of a dynamical system and the number of samples are crucial for the practical definition of noise variables among the signal variables generated by such a system.
1 code implementation • 28 Jun 2023 • Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake
This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function.