Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
A two-layer ICA-like model estimated by score matching
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Interpretation and generalization of score matching
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A two-layer model of natural stimuli estimated with score matching
Neural Computation
A connection between score matching and denoising autoencoders
Neural Computation
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Score matching (SM) and contrastive divergence (CD) are two recently proposed methods for estimation of nonnormalized statistical methods without computation of the normalization constant (partition function). Although they are based on very different approaches, we show in this letter that they are equivalent in a special case: in the limit of infinitesimal noise in a specific Monte Carlo method. Further, we show how these methods can be interpreted as approximations of pseudolikelihood.