no code implementations • 3 Oct 2023 • Xuran Meng, Difan Zou, Yuan Cao
Modern deep learning models are usually highly over-parameterized so that they can overfit the training data.
no code implementations • 31 Mar 2023 • Xuran Meng, Yuan Cao, Difan Zou
In this paper, we explore the per-example gradient regularization (PEGR) and present a theoretical analysis that demonstrates its effectiveness in improving both test error and robustness against noise perturbations.
no code implementations • 21 Aug 2022 • Xuran Meng, Jianfeng Yao, Yuan Cao
Recent works have demonstrated a double descent phenomenon in over-parameterized learning.
no code implementations • 26 Nov 2021 • Xuran Meng, Jianfeng Yao
A main contribution from the paper is that we identify the difficulty of the classification problem as a driving factor for the appearance of heavy tail in weight matrices spectra.