4 papers · 1 filter
How Accurate Is Accurate Enough?
Ningkang Peng, Qianfeng Yu, Jingyang Mao +2
How accurate must a numerical approximation be within a learning system? Primitive error alone cannot answer this question: errors of the same magnitude can have very different con…
Does a Shared Temperature Imply a Shared Angular Scale in Probabilistic Contrastive Learning?
Ningkang Peng, Qianfeng Yu, Jingyang Mao +4
In probabilistic contrastive learning, a shared temperature is commonly interpreted as a shared similarity scale, but this interpretation does not hold for high-dimensional distrib…
When Accuracy Is Not Enough: Uncertainty Collapse between Noisy Label Learning and Out-of-Distribution Detection
Ningkang Peng, Jingyang Mao, Runhan Zhou +2
Learning with noisy labels (LNL) is typically benchmarked by closed-set classification accuracy, yet deployment often requires classifiers to reject out-of-distribution (OOD) input…
Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
Ningkang Peng, Jingyang Mao, Xiaoqian Peng +2
Noisy-label methods often estimate sample reliability from forward-space signals such as loss, confidence, or entropy. These signals indicate whether a sample is difficult to predi…