论文标题
我没有MCMC：摊销采样，以快速，稳定的基于能量的模型的培训
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
论文作者
论文摘要
基于能量的模型（EBM）提出了一种灵活而有吸引力的代表不确定性的方式。尽管最近进步，但在最先进的方法是昂贵，不稳定的，并且需要大量的调整和域专业知识才能成功申请，但在高维数据上进行培训EBM仍然是一个具有挑战性的问题。在这项工作中，我们提出了一种规模训练EBM的简单方法，该方法使用熵调查的发电机来摊销EBM训练中通常使用的MCMC采样。我们改进了具有快速变化近似的先前基于MCMC的熵正则化方法。我们通过使用该方法来训练可拖动的可能性模型来证明我们的方法的有效性。接下来，我们将估计器应用于最近提出的联合能源模型（JEM），在该模型中，我们将原始性能与更快，更稳定的训练相匹配。这使我们能够将JEM模型扩展到来自各种连续域的表格数据的半监督分类。
Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable, and require considerable tuning and domain expertise to apply successfully. In this work, we present a simple method for training EBMs at scale which uses an entropy-regularized generator to amortize the MCMC sampling typically used in EBM training. We improve upon prior MCMC-based entropy regularization methods with a fast variational approximation. We demonstrate the effectiveness of our approach by using it to train tractable likelihood models. Next, we apply our estimator to the recently proposed Joint Energy Model (JEM), where we match the original performance with faster and stable training. This allows us to extend JEM models to semi-supervised classification on tabular data from a variety of continuous domains.
