Publications & Preprints

On the Surrogate Gap between Contrastive and Supervised Losses

Han Bao*, Yoshihiro Nagano*, Kento Nozawa* (*: equal contribution)

ICML, 2022

proceeding / arxiv / presentation / code

Complex Energies of the Coherent Logitudinal Optical Phonon-plasmon Coupled Mode According to Dynamic Mode Decomposition Analysis

Itsushi Sakata, Takuya Sakata, Kohji Mizoguchi, Satoshi Tanaka, Goro Oohata, Ichiro Akai, Yasuhiko Igarashi, Yoshihiro Nagano, Masato Okada

Scientific Reports, 2021


Analysis of Trainability of Gradient-based Multi-environment Learning from Gradient Norm Regularization Perspective

Shiro Takagi, Yoshihiro Nagano, Yuki Yoshida, Masato Okada

International Joint Conference on Neural Networks (IJCNN), 2021


Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student Networks

Haruka Asanuma, Shiro Takagi, Yoshihiro Nagano, Yuki Yoshida, Yasuhiko Igarashi, Masato Okada

Journal of the Physical Society of Japan, 2021

paper / arxiv

Collective dynamics of repeated inference in variational autoencoder rapidly find cluster structure

Yoshihiro Nagano, Ryo Karakida, Masato Okada

Scientific Reports, 2020


Normal mode analysis of a relaxation process with Bayesian inference

Itsushi Sakata, Yoshihiro Nagano, Yasuhiko Igarashi, Shin Murata, Kohji Mizoguchi, Ichiro Akai, and Masato Okada

Science and Technology of Advanced Materials, 2020


A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning

Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama

International Conference on Machine Learning (ICML), 2019

proceeding / arxiv / code / slide

Input Response of Neural Network Model with Lognormally Distributed Synaptic Weights

Yoshihiro Nagano, Ryo Karakida, Norifumi Watanabe, Atsushi Aoyama, Masato Okada

Journal of the Physical Society of Japan, 2016


Analysis of Neural Circuit for Visual Attention Using Lognormally Distributed Input

Yoshihiro Nagano, Norifumi Watanabe, Atsushi Aoyama

Artificial Neural Networks and Machine Learning (ICANN), 2014