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. Code is publicly
available at this https URL

本文提出了一种基于能量的生成流网络 (EB-GFN) 概率建模算法，该算法通过随机构造数据策略来建模生成过程，并将昂贵的 MCMC 探索在从 GFlowNet 中采样的固定数量的操作中分摊开来。我们展示了 GFlowNet 如何近似执行大块 Gibbs 采样以在多个模式之间混合。我们提出了一个框架，共同训练 GFlowNet 与能量函数，使 GFlowNet 学习从能量分布中采样，而能量则通过从 GFlowNet 中获得负样本的近似 MLE 目标进行学习。我们展示了 EB-GFN 在各种概率建模任务中的有效性。