77 citations · 371 across the 21 of their papers we have counts for
21 papers
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata +1
The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces. Prior works have proposed deep neural networks…
Meta-learning of Physics-informed Neural Networks for Efficiently Solving Newly Given PDEs
Tomoharu Iwata, Yusuke Tanaka, Naonori Ueda
We propose a neural network-based meta-learning method to efficiently solve partial differential equation (PDE) problems. The proposed method is designed to meta-learn how to solve…
Deep-learning Real/Bogus classification for the Tomo-e Gozen transient survey
Ichiro Takahashi, Ryo Hamasaki, Naonori Ueda +5
We present a deep neural network Real/Bogus classifier that improves classification performance in the Tomo-e Gozen transient survey by handling label errors in the training data.…
Excess risk analysis for epistemic uncertainty with application to variational inference
Futoshi Futami, Tomoharu Iwata, Naonori Ueda +2
Bayesian deep learning plays an important role especially for its ability evaluating epistemic uncertainty (EU). Due to computational complexity issues, approximation methods such…
Loss function based second-order Jensen inequality and its application to particle variational inference
Futoshi Futami, Tomoharu Iwata, Naonori Ueda +2
Bayesian model averaging, obtained as the expectation of a likelihood function by a posterior distribution, has been widely used for prediction, evaluation of uncertainty, and mode…
Photometric classification of HSC transients using machine learning
Ichiro Takahashi, Nao Suzuki, Naoki Yasuda +5
The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through…