The Recurrent Temporal Restricted Boltzmann Machine
NIPS(2008)
摘要
The Temporal Restricted Boltzmann Machine (TRBM) is a probabilistic model for sequences that is able to successfully model (i.e., generat e nice-looking samples of) several very high dimensional sequences, such as motion capture data and the pixels of low resolution videos of balls bouncing in a box. The major disadvan- tage of the TRBM is that exact inference is extremely hard, since even computing a Gibbs update for a single variable of the posterior is exponentially expensive. This difficulty has necessitated the use of a heuristic infer ence procedure, that nonetheless was accurate enough for successful learning. In this paper we intro- duce the Recurrent TRBM, which is a very slight modification o f the TRBM for which exact inference is very easy and exact gradient learning is almost tractable. We demonstrate that the RTRBM is better than an analogous TRBM at generating motion capture and videos of bouncing balls.
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关键词
probabilistic model,boltzmann machine,low resolution,motion capture
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