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Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style

Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. 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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