We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the training objective requires full trajectories to compute, resulting in sluggish credit assignment issues due to use of entire trajectories and a learning signal present only at the terminal time. In this work, we present Diffusion Generative Flow Samplers (DGFS), a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments, via parameterizing an additional "flow function". Our method takes inspiration from the theory developed for generative flow networks (GFlowNets), allowing us to make use of intermediate learning signals. Through various challenging experiments, we demonstrate that DGFS achieves more accurate estimates of the normalization constant than closely-related prior methods.

翻译：我们解决从难以处理的高维密度函数中采样的问题，这是机器学习和统计学中经常出现的一项基本任务。我们扩展了最新的基于采样的方法，这些方法利用受控随机过程来模拟来自这些目标密度的近似样本。这些方法的主要缺点是训练目标需要完整轨迹来计算，导致由于使用整个轨迹且仅在终止时刻存在学习信号而出现缓慢的信用分配问题。在本文中，我们提出了扩散生成流采样器（DGFS），这是一种基于采样的框架，通过参数化额外的“流函数”，学习过程可以可处理地分解为短的部分轨迹段。我们的方法借鉴了为生成流网络（GFlowNets）发展的理论，使我们能够利用中间学习信号。通过各类具有挑战性的实验，我们证明DGFS在归一化常数的估计精度上显著优于密切相关的前期方法。