Deep double descent: where bigger models and more data hurt*
نویسندگان
چکیده
Abstract We show that a variety of modern deep learning tasks exhibit ‘double-descent’ phenomenon where, as we increase model size, performance first gets worse and then better. Moreover, double descent occurs not just function but also the number training epochs. unify above phenomena by defining new complexity measure call effective conjecture generalized with respect to this measure. Furthermore, our notion allows us identify certain regimes where increasing (even quadrupling) train samples actually hurts test performance.
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ژورنال
عنوان ژورنال: Journal of Statistical Mechanics: Theory and Experiment
سال: 2021
ISSN: ['1742-5468']
DOI: https://doi.org/10.1088/1742-5468/ac3a74