12 citations · 13 across the 4 of their papers we have counts for
4 papers
Conformal-in-the-Loop for Learning with Imbalanced Noisy Data
John Brandon Graham-Knight, Jamil Fayyad, Nourhan Bayasi +2
Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world c…
Debiasify: Self-Distillation for Unsupervised Bias Mitigation
Nourhan Bayasi, Jamil Fayyad, Ghassan Hamarneh +2
Simplicity bias poses a significant challenge in neural networks, often leading models to favor simpler solutions and inadvertently learn decision rules influenced by spurious corr…
BiasPruner: Debiased Continual Learning for Medical Image Classification
Nourhan Bayasi, Jamil Fayyad, Alceu Bissoto +2
Continual Learning (CL) is crucial for enabling networks to dynamically adapt as they learn new tasks sequentially, accommodating new data and classes without catastrophic forgetti…
Model Compression Methods for YOLOv5: A Review
Mohammad Jani, Jamil Fayyad, Younes Al-Younes +1
Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the…