6 citations · 12 across the 9 of their papers we have counts for
4 papers · 1 filter
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi +2
Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number…
Active Learning for Noisy Data Streams Using Weak and Strong Labelers
Taraneh Younesian, Dick Epema, Lydia Y. Chen
Labeling data correctly is an expensive and challenging task in machine learning, especially for on-line data streams. Deep learning models especially require a large number of cle…
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Amirmasoud Ghiassi, Taraneh Younesian, Robert Birke +1
Robustness to label noise is a critical property for weakly-supervised classifiers trained on massive datasets. Robustness to label noise is a critical property for weakly-supervis…
QActor: On-line Active Learning for Noisy Labeled Stream Data
Taraneh Younesian, Zilong Zhao, Amirmasoud Ghiassi +2
Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media. Due to privacy concerns and governmental regu…