A method for quantifying the generalization capabilities of generative models for solving Ising models

arXiv:2405.03435v1 Announce Type: cross
Abstract: For Ising models with complex energy landscapes, whether the ground state can be found by neural networks depends heavily on the Hamming distance between the training datasets and the ground state. Despite the fact that various recently proposed generative models have shown good performance in solving Ising models, there is no adequate discussion on how to quantify their generalization capabilities. Here we design a Hamming distance regularizer in the framework of a class of generative …

abstract arxiv capabilities cond-mat.dis-nn cs.ai cs.lg datasets energy found generative generative models networks neural networks state training training datasets type