NIPS Proceedingsβ

Compete to Compute

Part of: Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Authors

Conference Event Type: Poster

Abstract

Local competition among neighboring neurons is common in biological neural networks (NNs). We apply the concept to gradient-based, backprop-trained artificial multilayer NNs. NNs with competing linear units tend to outperform those with non-competing nonlinear units, and avoid catastrophic forgetting when training sets change over time.