Training neural networks for and by interpolation
In modern supervised learning, many deep neural networks are able to interpolate the data: the empirical loss can be driven to near zero on all samples simultaneously. In this work, we explicitly exploit this interpolation property for the design of a new optimization algorithm for deep learning, wh...
| Main Authors: | , , |
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| 格式: | Conference item |
| 語言: | English |
| 出版: |
Journal of Machine Learning Research
2020
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