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Heskes, T.M.

We study on-line backpropagation and show that the existing theoretical descriptions are strictly valid only on relatively short time scales or in the vicinity of (local) minima of the backpropagation error potential. Qualitative global features (e.g., why is it much easier to escape from local minima than from global minima) may also be explained by these local descriptions, but the current approaches cannot give accurate quantitative predictions of global properties (e.g., how long does it take to reach the global minimum starting from a local minimum). 1 Introduction  On-line backpropagation stands for backpropagation where at each learning step one of the training patterns is drawn at random from the training set and presented to the network. This is in contrast with batch-mode backpropagation where a weight change takes place on account of the whole training set. The random pattern presentation in on-line backpropagation leads to a special kind of noise, which helps to escape from..

Tom Heskes

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Stochastics of on-Line Backpropagation

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Stochastics of on-line backpropagation

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.215

Stochastics of on-line backpropagation

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