12 citations · 14 across the 2 of their papers we have counts for
3 papers
cs.CV2020★ 2 cited
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
Xinwei Sun, Yilun Xu, Peng Cao +4
Fusing data from multiple modalities provides more information to train machine learning systems. However, it is prohibitively expensive and time-consuming to label each modality w…
cs.LG2019
L_DMI: An Information-theoretic Noise-robust Loss Function
Yilun Xu, Peng Cao, Yuqing Kong +1
Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly d…
cs.LG2019★ 12 cited
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Peng Cao, Yilun Xu, Yuqing Kong +1
Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: \em…