We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Building upon the theory of generative flow networks (GFlowNets), we model the generation process by a stochastic data construction policy and thus amortize expensive MCMC exploration into a fixed number of actions sampled from a GFlowNet. We show how GFlowNets can approximately perform large-block Gibbs sampling to mix between modes. We propose a framework to jointly train a GFlowNet with an energy function, so that the GFlowNet learns to sample from the energy distribution, while the energy learns with an approximate MLE objective with negative samples from the GFlowNet. We demonstrate EB-GFN's effectiveness on various probabilistic modeling tasks.

翻译：我们提出基于能源的基因流动网络(EB-GFN),这是用于高维离散数据的一种新颖的概率模型算法。根据基因流动网络(GFlowNets)的理论,我们用随机数据构建政策来模拟生成过程,从而将昂贵的MCMC勘探活动摊还到从GFlowNet抽样的固定数量的行动中。我们展示了GFlowNets如何在各种模式之间大致进行大块Gibs取样。我们提议了一个联合培训具有能源功能的GFlowNet的框架,以便GFlowNet能够学习能源分布的样本,而能源则以GFlowNet的负面样本来学习MLE的近似MLE目标。我们展示了EB-GFN在各种概率建模任务上的有效性。