Memorization in deep learning: A survey

J Wei, Y Zhang, LY Zhang, M Ding, C Chen… - ar** against label noise without validation data
S Yuan, L Feng, T Liu - ar** 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 …

Late stop**: Avoiding confidently learning from mislabeled examples

S Yuan, L Feng, T Liu - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
Sample selection is a prevalent method in learning with noisy labels, where small-loss data
are typically considered as correctly labeled data. However, this method may not effectively …

Triage: Characterizing and auditing training data for improved regression

N Seedat, J Crabbé, Z Qian… - Advances in Neural …, 2023 - proceedings.neurips.cc
Data quality is crucial for robust machine learning algorithms, with the recent interest in data-
centric AI emphasizing the importance of training data characterization. However, current …