1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Early Stopping Against Label Noise Without Validation Data
Suqin Yuan, Lei Feng, Tongliang Liu
Early stopping methods in deep learning face the challenge of balancing the volume of training and validation data, especially in the presence of label noise. Concretely, sparing m…
cs.LG2025★ 1 cited
Instance-dependent Early Stopping
Suqin Yuan, Runqi Lin, Lei Feng +2
In machine learning practice, early stopping has been widely used to regularize models and can save computational costs by halting the training process when the model's performance…
cs.LG2025
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
Suqin Yuan, Lei Feng, Bo Han +1
Sample selection is a prevalent approach in learning with noisy labels, aiming to identify confident samples for training. Although existing sample selection methods have achieved…