Learning the Ising model with generative neural networks
Francesco D’Angelo, Lucas Böttcher · Physical Review Research · 2020
This paper studies the representational properties of restricted Boltzmann machines and variational autoencoders in terms of their ability to capture physical features of the Ising model. The authors provide a detailed analysis of different network architectures and training algorithms, and identify significant differences in the learning performance of both probabilistic models