Learning Activation Functions to Improve Deep Neural Networks

Forest Agostinelli; Matthew Hoffman; Peter Sadowski; Pierre Baldi

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arXiv · 1412.6830

Learning Activation Functions to Improve Deep Neural Networks

Forest Agostinelli·Matthew Hoffman·Peter Sadowski·Pierre Baldi

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Abstract

Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is learned independently for each neuron using gradient descent. With this adaptive activation function, we are able to improve upon deep neural network architectures composed of static rectified linear units, achieving state-of-the-art performance on CIFAR-10 (7.51%), CIFAR-100 (30.83%), and a benchmark from high-energy physics involving Higgs boson decay modes.

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BibTeX RIS

Forest Agostinelli, Matthew Hoffman, Peter Sadowski, Pierre Baldi. 2014-12-21. Learning Activation Functions to Improve Deep Neural Networks. https://arxiv.org/abs/1412.6830

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