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cs.LG2025
How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
Wei Huang, Andi Han, Yujin Song +4
The capacity of deep learning models is often large enough to both learn the underlying statistical signal and overfit to noise in the training set. This noise memorization can be…
cs.LG2025
Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel
Yilan Chen, Zhichao Wang, Wei Huang +3
Gradient-based optimization methods have shown remarkable empirical success, yet their theoretical generalization properties remain only partially understood. In this paper, we est…
cs.LG2024
On the Emergence of Cross-Task Linearity in the Pretraining-Finetuning Paradigm
Zhanpeng Zhou, Zijun Chen, Yilan Chen +2
The pretraining-finetuning paradigm has become the prevailing trend in modern deep learning. In this work, we discover an intriguing linear phenomenon in models that are initialize…