基于非平衡热力学的深度无监督学习
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
摘要：
机器学习中的一个中心问题涉及使用高度灵活的概率分布族对复杂数据集进行建模，其中学习，采样，推理和评估仍然是分析或计算上易于处理的。在这里，我们开发了一种同时实现灵活性和易处理性的方法。受非平衡统计物理学的启发，其基本思想是通过迭代向前扩散过程系统地、缓慢地破坏数据分布中的结构。然后，我们学习一个反向扩散过程，恢复数据中的结构，产生一个高度灵活和易于处理的数据生成模型。这种方法使我们能够快速学习，采样和评估具有数千层或时间步长的深度生成模型中的概率，以及在学习模型下计算条件和后验概率。我们还发布了该算法的开源参考实现。
英文摘要：
A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.
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