Stronger generalization bounds for deep nets via a compression approach

ICML, pp. 254-263, 2018.

Cited by: 306|Views88
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Abstract:

Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter counting. The current paper shows generalization bounds thatu0027re orders of magn...More

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