17 citations · 42 across the 7 of their papers we have counts for
4 papers · 1 filter
Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets
Yingsong Huang, Bing Bai, Shengwei Zhao +2
Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model outp…
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs
Jian Liang, Fangrui Lv, Di Liu +5
In this paper, we focus on learning effective entity matching models over multi-source large-scale data. For real applications, we relax typical assumptions that data distributions…
Why Attentions May Not Be Interpretable?
Bing Bai, Jian Liang, Guanhua Zhang +3
Attention-based methods have played important roles in model interpretations, where the calculated attention weights are expected to highlight the critical parts of inputs~(e.g., k…
Adversarial Infidelity Learning for Model Interpretation
Jian Liang, Bing Bai, Yuren Cao +2
Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characte…