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On efficiently computable functions, deep networks and sparse compositionality
(Center for Brains, Minds and Machines (CBMM), 2025-02-01)In previous papers [4, 6] we have claimed that for each function which is efficiently Turing computable there exists a deep and sparse network which approximates it arbitrarily well. We also claimed a key role for ... -
Self-Assembly of a Biologically Plausible Learning Circuit
(Center for Brains, Minds and Machines (CBMM), 2024-12-28)Over the last four decades, the amazing success of deep learning has been driven by the use of Stochastic Gradient Descent (SGD) as the main optimization technique. The default implementation for the computation of the ... -
On Generalization Bounds for Neural Networks with Low Rank Layers
(Center for Brains, Minds and Machines (CBMM), 2024-10-11)While previous optimization results have suggested that deep neural networks tend to favour low-rank weight matrices, the implications of this inductive bias on generalization bounds remain under-explored. In this paper, ...
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