Paper
Conditional Restricted Boltzmann Machines for Structured Output\n Prediction
Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic\nmodels that have recently been applied to a wide range of problems, including\ncollaborative filtering, classification, and modeling motion capture data.\nWhile much progress has been made in training non-conditional RBMs, these\nalgorithms are not applicable to conditional models and there has been almost\nno work on training and generating predictions from conditional RBMs for\nstructured output problems. We first argue that standard Contrastive\nDivergence-based learning may not be suitable for training CRBMs. We then\nidentify two distinct types of structured output prediction problems and\npropose an improved learning algorithm for each. The first problem type is one\nwhere the output space has arbitrary structure but the set of likely output\nconfigurations is relatively small, such as in multi-label classification. The\nsecond problem is one where the output space is arbitrarily structured but\nwhere the output space variability is much greater, such as in image denoising\nor pixel labeling. We show that the new learning algorithms can work much\nbetter than Contrastive Divergence on both types of problems.\n
Authors: Mnih, Volodymyr · Larochelle, Hugo · Hinton, Geoffrey E.