ICML 2022spotlight30 citations
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki, Amartya Mitra, Yoshua Bengio, Guillaume Lajoie
Abstract
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
BibTeX
@InProceedings{pmlr-v162-pezeshki22a,
title = {Multi-scale Feature Learning Dynamics: Insights for Double Descent},
author = {Pezeshki, Mohammad and Mitra, Amartya and Bengio, Yoshua and Lajoie, Guillaume},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {17669--17690},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/pezeshki22a/pezeshki22a.pdf},
url = {https://proceedings.mlr.press/v162/pezeshki22a.html},
abstract = {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