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Compete to Compute
2013
Neural Information Processing Systems
Local competition among neighboring neurons is common in biological neural networks (NNs). In this paper, 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.
dblp:conf/nips/SrivastavaMKGS13
fatcat:pczs4rl76ndazcf7egn5seidmu