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2017

Auto-Encoding Sequential Monte Carlo

Le, Tuan Anh, Igl, Maximilian, Rainforth, Tom et al.

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We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models.

Our approach relies on the efficiency of sequential Monte Carlo (SMC) for performing inference in structured probabilistic models and the flexibility of deep neural networks to model complex conditional probability distributions.
We develop additional theoretical insights and introduce a new training procedure which improves both model and proposal learning.
We demonstrate that our approach provides a fast, easy-to-implement and scalable means for simultaneous model learning and proposal adaptation in deep generative models.

Auto-Encoding Sequential Monte Carlo · Around

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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Feynman-Kac formulae: genealogical and interacting particle systems with applications
P Del Moral · 2004
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A tutorial on particle filtering and smoothing: Fifteen years later
Arnaud Doucet and Adam M Johansen · 2009
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Then

Auto-encoding sequential Monte Carlo
Original
Tuan Anh Le, Maximilian Igl, Tom Jin, Tom Rainforth, and Frank Wood · 2017
Closest in time.
Filtering variational objectives
Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, and Yee Teh · 2017
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Variational sequential Monte Carlo
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