论文标题
使用基于能量的模型重建成对相互作用
Reconstruction of Pairwise Interactions using Energy-Based Models
论文作者
论文摘要
诸如ISING模型或广义POTTS模型之类的成对模型在物理，生物学和经济学等领域中发现了许多成功的应用。紧密连接的是反统计力学的问题，其中的目标是推论所观察到的数据的此类模型的参数。该领域的一个开放问题是在数据包含成对模型中不存在的其他高阶交互的情况下如何训练这些模型的问题。在这项工作中，我们提出了一种基于基于能量的模型和伪虫期最大化来解决这些并发症的方法：我们表明，将成对模型和神经网络结合的混合模型可以从成对相互作用的重建中显着改善。与仅使用成对模型和仅使用神经网络的方法相比，我们表明了这些改进，可以始终如一地保持。这与一个普遍的想法一致，即简单的可解释模型和复杂的黑盒模型不一定是二分法：插值这两类模型可以使两者保持一定优势。
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.
