70 citations · 110 across the 6 of their papers we have counts for
6 papers
Comprehensive studies on the universality of BKT transitions -- Machine-learning study, Monte Carlo simulation, and Level-spectroscopy method
Hiromi Otsuka, Kenta Shiina, Yutaka Okabe
Comprehensive studies are made on the six-state clock universality of two models using several approaches. We apply the machine-learning technique of phase classification to the an…
Super-resolution of spin configurations based on flow-based generative models
Kenta Shiina, Lee Hwee Kuan, Hiroyuki Mori +2
We present a super-resolution method for spin systems using a flow-based generative model that is a deep generative model with reversible neural network architecture. Starting from…
Weakly-supervised learning on Schrodinger equation
Kenta Shiina, Hwee Kuan Lee, Yutaka Okabe +1
We propose a machine learning method to solve Schrodinger equations for a Hamiltonian that consists of an unperturbed Hamiltonian and a perturbation. We focus on the cases where th…
Inverse Renormalization Group based on Image Super-Resolution using Deep Convolutional Networks
Kenta Shiina, Hiroyuki Mori, Yusuke Tomita +2
The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration in…
Machine-Learning Study using Improved Correlation Configuration and Application to Quantum Monte Carlo Simulation
Yusuke Tomita, Kenta Shiina, Yutaka Okabe +1
We use the Fortuin-Kasteleyn representation based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-lea…
Machine-Learning Studies on Spin Models
Kenta Shiina, Hiroyuki Mori, Yutaka Okabe +1
With the recent developments in machine learning, Carrasquilla and Melko have proposed a paradigm that is complementary to the conventional approach for the study of spin models. A…