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Bernardo Huberman

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2 papers

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NeurIPS Conference 1990 Conference Paper

Generalization by Weight-Elimination with Application to Forecasting

Andreas Weigend
David Rumelhart
Bernardo Huberman

Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates.

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NeurIPS Conference 1987 Conference Paper

A Dynamical Approach to Temporal Pattern Processing

W. Stornetta
Tad Hogg
Bernardo Huberman

Recognizing patterns with temporal context is important for such tasks as speech recognition, motion detection and signature verification. We propose an architecture in which time serves as its own representation, and temporal context is encoded in the state of the nodes. We contrast this with the approach of replicating portions of the architecture to represent time. As one example of these ideas, we demonstrate an architecture with capacitive inputs serving as temporal feature detectors in an otherwise standard back propagation model. Experiments involving motion detection and word discrimination serve to illustrate novel features of the system. Finally, we discuss possible extensions of the architecture.

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