2 papers
stat.ML2024
Ratio Divergence Learning Using Target Energy in Restricted Boltzmann Machines: Beyond Kullback--Leibler Divergence Learning
Yuichi Ishida, Yuma Ichikawa, Aki Dote +2
We propose ratio divergence (RD) learning for discrete energy-based models, a method that utilizes both training data and a tractable target energy function. We apply RD learning t…
cond-mat.stat-mech2024
Effect of constraint relaxation on dynamic critical phenomena in minimum vertex cover problem
Aki Dote, Koji Hukushima
The effects of constraint relaxation on dynamic critical phenomena in the Minimum Vertex Cover (MVC) problem on ErdÅs-Rényi random graphs are investigated using Markov chain Mont…