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Kyle Whitecross

2 accepted papers

2024

Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise

UAI 2024poster

Increasing the size of overparameterized neural networks has been a key in achieving state-of-the-art performance. This is captured by the double descent phenomenon, where the test loss follows a decreasing-increasing-decreasing pattern (or sometimes monotonically decreasing) as model width increase…

Cited by 3SourcePDFScholar
2022

Investigating Why Contrastive Learning Benefits Robustness against Label Noise

ICML 2022spotlight

Self-supervised Contrastive Learning (CL) has been recently shown to be very effective in preventing deep networks from overfitting noisy labels. Despite its empirical success, the theoretical understanding of the effect of contrastive learning on boosting robustness is very limited. In this work, w…