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arXiv · 2008.00938

Implicit Regularization via Neural Feature Alignment

Aristide Baratin·Thomas George·César Laurent·R Devon Hjelm·Guillaume Lajoie·Pascal Vincent·Simon Lacoste-Julien
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Abstract

We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a regularization effect induced by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. This can be interpreted as a combined mechanism of feature selection and compression. By extrapolating a new analysis of Rademacher complexity bounds for linear models, we motivate and study a heuristic complexity measure that captures this phenomenon, in terms of sequences of tangent kernel classes along optimization paths.

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Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien. 2020-08-03. Implicit Regularization via Neural Feature Alignment. https://arxiv.org/abs/2008.00938

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