Classical generalization bounds are surprisingly tight for Deep Networks
Deep networks are usually trained and tested in a regime in which the training classification error is not a good predictor of the test error. Thus the consensus has been that generalization, defined as convergence of the empirical to the expected error, does not hold for deep networks. Here we show...
| Main Authors: | , , , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM)
2018
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| Online Access: | http://hdl.handle.net/1721.1/116911 |