2 citations · 5 across the 5 of their papers we have counts for
6 papers
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Cosmin Octavian Pene, Amirmasoud Ghiassi, Taraneh Younesian +2
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in mul…
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…
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…
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Amirmasoud Ghiassi, Robert Birke, Rui Han +1
Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant por…
Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation
Mostafa Hadadian Nejad Yousefi, Amirmasoud Ghiassi, Boshra Sadat Hashemi +1
Smart devices have become an indispensable part of our lives and gain increasing applicability in almost every area. Latency-aware applications such as Augmented Reality (AR), auto…
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…