# Multi-scale Feature Learning Dynamics: Insights for Double Descent

@inproceedings{Pezeshki2021MultiscaleFL, title={Multi-scale Feature Learning Dynamics: Insights for Double Descent}, author={Mohammad Pezeshki and Amartya Mitra and Yoshua Bengio and Guillaume Lajoie}, booktitle={International Conference on Machine Learning}, year={2021} }

An intriguing phenomenon that arises from the high-dimensional learning dynamics of neural networks is the phenomenon of “double descent”. The more commonly studied aspect of this phenomenon corresponds to model-wise double descent where the test error exhibits a second descent with increasing model complexity, beyond the classical U-shaped error curve. In this work, we investigate the origins of the less studied epoch-wise double descent in which the test error undergoes two non-monotonous…

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