Gradient descent learning in and out of equilibrium.
Relations between the off thermal equilibrium dynamical process of on-line learning and the thermally equilibrated off-line learning are studied for potential gradient descent learning. The approach of Opper to study on-line Bayesian algorithms is used for potential based or maximum likelihood learning. We look at the on-line learning algorithm that best approximates the off-line algorithm in the sense of least Kullback-Leibler information loss. The closest on-line algorithm works by updating the weights along the gradient of an effective potential, which is different from the parent off-line potential. A few examples are analyzed and the origin of the potential annealing is discussed.
Caticha
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Instituto de Física, Universidade de São Paulo, CP 66318 São Paulo, SP, CEP05389-970 Brazil.
Araújo de Oliveira
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D000465:Algorithms; D001499:Bayes Theorem; D001703:Biophysics / Q000379:methods*; D007858:Learning; D016013:Likelihood Functions; D015233:Models, Statistical; D016571:Neural Networks, Computer; D016011:Normal Distribution