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Direct Feedback Alignment Provides Learning in Deep Neural Networks
[article]
2016
arXiv
pre-print
Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer by layer, from the output layer to the hidden layers. A recently discovered method called feedback-alignment shows that the weights used for propagating the error backward don't have to be symmetric with the weights used for propagation the activation forward. In fact, random feedback weights work evenly well, because the
arXiv:1609.01596v5
fatcat:xi4c6dsj7zhbdjy7raw3xy5gpq