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Implicit Regularization via Neural Feature Alignment
[article]
2021
arXiv
pre-print
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
arXiv:2008.00938v3
fatcat:xtcsbf4kcnbn3itixjq3ddrwhy