Implicit Regularization via Neural Feature Alignment

• URL: http://arxiv.org/abs/2008.00938v3
• Date: Wed, 17 Mar 2021 00:57:56 GMT
• Title: Implicit Regularization via Neural Feature Alignment
• Authors: Aristide Baratin, Thomas George, C\'esar Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien
• Abstract summary: We highlight a regularization effect induced by a dynamical alignment of neural tangent features. By extrapolating a new analysis of Rademacher complexity bounds for linear models, we motivate and study a complexity measure that captures this phenomenon.
• Score: 39.257382575749354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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