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EnglishThis report introduces a novel algorithm to learn the width of non-linear activation functions (of arbitrary analytical form) in layered networks. The algorithm is based on a steepest gradient-descent technique, and relies on the inductive proof of a theorem that involves the novel concept of expansion function of the activation associated to a given unit of the neural net. Experimental results obtained in a speaker nomalization task with a mixture of Multilayer Perceptron show a dramatic improvement of performance with respect to the standard Back-Propagation training
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| Titolo: | Learning the Width of Activations in Neural Networks |
| Autori: | |
| Data di pubblicazione: | 1996 |
| Abstract: | This report introduces a novel algorithm to learn the width of non-linear activation functions (of arbitrary analytical form) in layered networks. The algorithm is based on a steepest gradient-descent technique, and relies on the inductive proof of a theorem that involves the novel concept of expansion function of the activation associated to a given unit of the neural net. Experimental results obtained in a speaker nomalization task with a mixture of Multilayer Perceptron show a dramatic improvement of performance with respect to the standard Back-Propagation training |
| Handle: | http://hdl.handle.net/11582/1296 |
| Appare nelle tipologie: | 5.12 Altro |
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http://hdl.handle.net/11582/1296
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