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

TL;DR

This paper introduces a novel approach combining Ornstein-Zernike theory with deep learning to improve inverse Ising inference, demonstrating superior performance and generalization on both simulated and real datasets.

Contribution

It proposes a deep neural network framework that learns the closure relation in inverse Ising inference, outperforming traditional methods and enhancing data efficiency and generalization.

Findings

Neural network outperforms field-theoretic expansions.

Model is more data-efficient than pseudolikelihood methods.

Accurately predicts from synthetic to real-world data.

Abstract

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 Ising problem and the Ornstein-Zernike formalism in liquid state physics. Rather than analytically deriving the closure relation, we use a deep neural network to learn the closure from simulations of the Ising model. We show, using simulations as well as biochemical datasets, that the deep neural network model outperforms systematic field-theoretic expansions, is more data-efficient than the pseudolikelihood method, and can generalize well beyond the parameter regime of the training data. The neural network is able to learn from synthetic data, which can be generated with relative ease, to give accurate predictions on real world datasets.