Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style

author: Graham Taylor, School of Engineering, University of Guelph
published: Aug. 26, 2009,   recorded: June 2009,   views: 801
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Description

The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We present a new model, based on the CRBM that preserves its most important computational properties and includes multiplicative three-way interactions that allow the effective interaction weight between two units to be modulated by the dynamic state of a third unit. We factorize the three-way weight tensor implied by the multiplicative model, reducing the number of parameters from O(N^3) to O(N^2). The result is an efficient, compact model whose effectiveness we demonstrate by modeling human motion. Like the CRBM, our model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve the model's ability to blend motion styles or to transition smoothly between them.

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