A General Method for Amortizing Variational Filtering
By Joseph Marino, Milan Cvitkovic, Yisong Yue. 2018
39 citations recorded by the source corpus.
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 inference performance across several recent deep dynamical latent variable models.
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- Sergey Levine
- Josh Tenenbaum
- Chelsea Finn
- Jiajun Wu
- Yoshua Bengio
- Michael Janner
- Pieter Abbeel
- Abhinav Kumar Gupta
Citations in the corpus
- Entity Abstraction in Visual Model-Based Reinforcement Learning (2019, methodology): “We therefore incorporate actions into the amortized varitional filtering framework [7] to develop an interactive inference algorithm (Appdx.”