Generalized Transformation-based Gradient

Autor:	Wu, Anbang, Chen, Shuangxi, Wu, Chunming
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Mathematics - Optimization and Control Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	The reparameterization trick has become one of the most useful tools in the field of variational inference. However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution functions or are expressible as deterministic transformations of such distributions. In this paper, we generalized the reparameterization trick by allowing a general transformation. We discover that the proposed model is a special case of control variate indicating that the proposed model can combine the advantages of CV and generalized reparameterization. Comment: There is some errors in the proof to the conclusion, therefore leading to untrusted conclusion, so I want to withdraw this paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.02681 Zobrazit plný text záznamu View this record from Arxiv