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Status: Unchecked ×Keyword: convergence time ×Clear all

3 claims from 1 paper

1. Computer Science › Neural Networks and Applications

   Unsupervised feature learning from finite data by message passing: Discontinuous versus continuous phase transition

   Huang and Toyoizumi · Physical review. E · 2016

   The authors study unsupervised learning from finite data with a restricted Boltzmann machine, using a message passing algorithm, and examine phase transitions, entropy and convergence time, comparing with a Hopfield model.

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   1. UncheckedIn this study of unsupervised learning, a salient feature needs only a few data to be learned, while a weak feature needs extensively many.“Our analysis reveals that the learning requires only a few data if the feature is salient and extensively many if the feature is weak.”
   2. UncheckedAs training data grows, the entropy of candidate features falls and turns negative before message passing fails, hinting at a discontinuous phase transition.“Moreover, the entropy of candidate features monotonically decreases with data size and becomes negative (i.e., entropy crisis) before the message passing becomes unstable, suggesting a discontinuous phase transition.”
   3. UncheckedAs the amount of training data grows, the message passing algorithm in this unsupervised learning setting is slow to converge only at intermediate data sizes.“In terms of convergence time of the message passing algorithm, the unsupervised learning exhibits an easy-hard-easy phenomenon as the training data size increases.”