Inferring a generative model from data is a fundamental problem in machine learning. It is well-known that the Ising model is the maximum entropy model for binary variables which reproduces the sample mean and pairwise correlations. Learning the parameters of the Ising model from data is the challenge. We establish an analogy between the inverse Is...

Inverse Ising inference by combining Ornstein-Zernike theory with deep learning