Layerwise self-supervised local rules learn the hierarchical structure of the Random Hierarchy Model as data-efficiently as supervised backpropagation, while direct feedback approximations fail due to missing masking nonlinearities.
(Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc
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Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
Layerwise self-supervised local rules learn the hierarchical structure of the Random Hierarchy Model as data-efficiently as supervised backpropagation, while direct feedback approximations fail due to missing masking nonlinearities.