research
∙
08/04/2021
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classif...
share
research
∙
11/13/2020
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Labeling real-world datasets is time consuming but indispensable for sup...
research
∙
10/27/2020
Active Learning for Noisy Data Streams Using Weak and Strong Labelers
Labeling data correctly is an expensive and challenging task in machine ...
research
∙
07/13/2020
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Robustness to label noise is a critical property for weakly-supervised c...
research
∙
01/28/2020
