论文标题
MCMC应该混合：基于能源的模型与神经传输潜在空间MCMC
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
论文作者
论文摘要
基于学习能量的模型（EBM）需要将学习模型作为学习算法的内部循环采样。但是，高维数据空间中EBM的MCMC采样通常不是混合的，因为在数据空间中，通常由深网络进行参数的能量函数是高度多模式的。这是EBM的理论和实践的严重障碍。在本文中，我们建议学习具有基于流量的模型（或一般是潜在变量模型）的EBM，以用作骨干，以便EBM是基于流的模型的校正或指数倾斜。我们表明，该模型在主干模型的潜在变量的空间中具有特别简单的形式，而潜在空间中EBM的MCMC采样充分混合并遍历数据空间中的模式。这可以适当地对EBM进行采样和学习。
Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by a deep network, is highly multi-modal in the data space. This is a serious handicap for both theory and practice of EBMs. In this paper, we propose to learn an EBM with a flow-based model (or in general a latent variable model) serving as a backbone, so that the EBM is a correction or an exponential tilting of the flow-based model. We show that the model has a particularly simple form in the space of the latent variables of the backbone model, and MCMC sampling of the EBM in the latent space mixes well and traverses modes in the data space. This enables proper sampling and learning of EBMs.
