基于能量模型的两两相互作用重构
Reconstruction of Pairwise Interactions using Energy-Based Models
摘要：
成对模型如伊辛模型或广义波茨模型在物理学、生物学和经济学等领域都有许多成功的应用。与此紧密相连的是逆统计力学问题，其目标是根据观测数据推断出这些模型的参数。在这个领域的一个开放的问题是如何训练这些模型的情况下，数据包含额外的高阶相互作用，不存在于成对模型的问题。在这项工作中，我们提出了一种基于能量的模型和pseudolikestrine最大化的方法来解决这些复杂性：我们表明，混合模型，它结合了成对模型和神经网络，可以导致显着改善成对相互作用的重建。我们表明，这些改进保持一致的标准方法相比，只使用成对模型和只使用神经网络的方法。这与简单的可解释模型和复杂的黑盒模型不一定是二分法的一般思想是一致的：对这两类模型进行插值可以保留两者的一些优点。
英文摘要：
Pairwise models like the Ising model or the generalized Potts model have found many successful applications in fields like physics, biology, and economics. Closely connected is the problem of inverse statistical mechanics, where the goal is to infer the parameters of such models given observed data. An open problem in this field is the question of how to train these models in the case where the data contain additional higher-order interactions that are not present in the pairwise model. In this work, we propose an approach based on Energy-Based Models and pseudolikelihood maximization to address these complications: we show that hybrid models, which combine a pairwise model and a neural network, can lead to significant improvements in the reconstruction of pairwise interactions. We show these improvements to hold consistently when compared to a standard approach using only the pairwise model and to an approach using only a neural network. This is in line with the general idea that simple interpretable models and complex black-box models are not necessarily a dichotomy: interpolating these two classes of models can allow to keep some advantages of both.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
