[1908.01580] The HSIC Bottleneck: Deep Learning without Back-Propagation (2019)
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> we show that it is possible to learn classification tasks at near competitive accuracy **without backpropagation**, by maximizing a surrogate of the mutual information between hidden representations and labels and simultaneously minimizing the mutual dependency between hidden representations and the inputs... the hidden units of a network trained in this way form useful representations. Specifically, fully competitive accuracy can be obtained by freezing the network trained without backpropagation and appending and training a one-layer network using conventional SGD to convert convert the representation to the desired format. The training method uses an approximation of the [#information bottleneck](/tag/information_bottleneck_method). Advantages: > - The method facilitates parallel processing and requires significantly less operations. > - It does not suffer from exploding or vanishing gradients. > - It is biologically more plausible than Backpropagation
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