A General Method for Amortizing Variational Filtering

Autor:	Marino, Joseph, Cvitkovic, Milan, Yue, Yisong
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step. By performing each inference optimization procedure with an iterative amortized inference model, we obtain a computationally efficient implementation of the algorithm, which we call amortized variational filtering. We present experiments demonstrating that this general-purpose method improves performance across several deep dynamical latent variable models. Comment: Advances in Neural Information Processing Systems (NIPS) 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.05090 Zobrazit plný text záznamu View this record from Arxiv