Universal Approximation of Residual Flows in Maximum Mean Discrepancy
University of California San Diego（加州大学圣地亚哥分校）
英文摘要：
Normalizing flows are a class of flexible deep generative models that offer easy likelihood computation. Despite their empirical success, there is little theoretical understanding of their expressiveness. In this work, we study residual flows, a class of normalizing flows composed of Lipschitz residual blocks. We prove residual flows are universal approximators in maximum mean discrepancy. We provide upper bounds on the number of residual blocks to achieve approximation under different assumptions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
