Factored conditional restricted Boltzmann Machines for modeling motion style

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 factor the threeway weight tensor implied by the multiplicative model, reducing the number of parameters from O(N3 ) to O(N2 ). 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 among them.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Graham W. Taylor, Geoffrey E. Hinton
• Keywords: ICML 2009, Machine Learning, Multiplicative Model, Multiplicative Three-way Interactions, Threeway Weight Tensor