{"id":684696,"date":"2020-08-11T13:08:50","date_gmt":"2020-08-11T20:08:50","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=684696"},"modified":"2020-08-11T13:08:50","modified_gmt":"2020-08-11T20:08:50","slug":"implicit-regularization-in-deep-learning-a-view-from-function-space","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/implicit-regularization-in-deep-learning-a-view-from-function-space\/","title":{"rendered":"Implicit Regularization in Deep Learning: A View from Function Space"},"content":{"rendered":"

We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a possible 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. By extrapolating a new analysis of Rademacher complexity bounds in linear models, we propose and study a new heuristic complexity measure for neural networks which captures this phenomenon, in terms of sequences of tangent kernel classes along in the learning trajectories.<\/p>\n","protected":false},"excerpt":{"rendered":"

We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a possible 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. By extrapolating a new analysis of Rademacher complexity bounds in linear models, we 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