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Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilisticmodels that have recently been applied to a wide range of problems, includingcollaborative filtering, classification, and modeling motion capture data.While much progress has been made in training non-conditional RBMs, thesealgorithms are not applicable to conditional models and there has been almostno work on training and generating predictions from conditional RBMs forstructured output problems. We first argue that standard ContrastiveDivergence-based learning may not be suitable for training CRBMs. We thenidentify two distinct types of structured output prediction problems andpropose an improved learning algorithm for each. The first problem type is onewhere the output space has arbitrary structure but the set of likely outputconfigurations is relatively small, such as in multi-label classification. Thesecond problem is one where the output space is arbitrarily structured butwhere the output space variability is much greater, such as in image denoisingor pixel labeling. We show that the new learning algorithms can work muchbetter than Contrastive Divergence on both types of problems.

Conditional Restricted Boltzmann Machines for Structured Output Prediction