Random feedback alignment algorithms to train neural networks: why do they align?
Dominique Chu,
Florian Bacho
Abstract:Feedback alignment algorithms are an alternative to backpropagation to train neural networks, whereby some of the partial derivatives that are required to compute the gradient are replaced by random terms. This essentially transforms the update rule into a random walk in weight space. Surprisingly, learning still works with those algorithms, including training of deep neural networks. The performance of FA is generally attributed to an alignment of the update of the random walker with the true gradient -… Show more
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