This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move - in the ensuing schemes - is to place priors on the selection of models, based upon expected free energy. In this setting, expected free energy reduces to a constrained mutual information, where the constraints inherit from priors over outcomes (i.e., preferred outcomes). The resulting scheme is first used to perform image classification on the MNIST dataset to illustrate the basic idea, and then tested on a more challenging problem of discovering models with dynamics, using a simple sprite-based visual disentanglement paradigm and the Tower of Hanoi (cf., blocks world) problem. In these examples, generative models are constructed autodidactically to recover (i.e., disentangle) the factorial structure of latent states - and their characteristic paths or dynamics.

翻译：本文关注离散生成模型的结构学习或发现。重点探讨贝叶斯模型选择与训练数据或内容的同化过程，尤其强调数据摄入的顺序。在后续提出的方案中，一个关键举措是基于预期自由能对模型选择施加先验。在此框架下，预期自由能可简化为受约束的互信息，其中约束继承自对结果（即偏好结果）的先验。该方案首先在MNIST数据集上执行图像分类以阐释基本思想，随后通过简单的精灵基元视觉解耦范式和汉诺塔（即积木世界）问题，在更具挑战性的含动力学模型发现任务中进行测试。在这些实例中，生成模型以自教方式构建，旨在恢复（即解耦）潜在状态的因子结构及其特征路径或动力学过程。