454 citations · 674 across the 15 of their papers we have counts for
5 papers · 1 filter
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han, Gang Niu, Xingrui Yu +4
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end. Although equipped with corrections for noisy labels,…
Clipped Matrix Completion: A Remedy for Ceiling Effects
Takeshi Teshima, Miao Xu, Issei Sato +1
We consider the problem of recovering a low-rank matrix from its clipped observations. Clipping is conceivable in many scientific areas that obstructs statistical analyses. On the…
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
Miao Xu, Gang Niu, Bo Han +3
We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries. An existing method concatenated $\X$ and $\Y…
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu +5
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during train…
Active Feature Acquisition with Supervised Matrix Completion
Sheng-Jun Huang, Miao Xu, Ming-Kun Xie +3
Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature…