7 citations · 10 across the 5 of their papers we have counts for
6 papers
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Yasir Ghunaim, Andrés Villa, Gergo Ignacz +3
Advancements in machine learning for molecular property prediction have improved accuracy but at the expense of higher computational cost and longer training times. Recently, the J…
On the Importance of Pretraining Data Alignment for Atomic Property Prediction
Yasir Ghunaim, Hasan Abed Al Kader Hammoud, Bernard Ghanem
This paper challenges the recent paradigm in atomic property prediction that links progress to growing dataset sizes and computational resources. We show that pretraining on a care…
Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction
Yasir Ghunaim, Robert Hoehndorf
Pre-training machine learning models on molecular properties has proven effective for generating robust and generalizable representations, which is critical for advancements in dru…
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging
Kumail Alhamoud, Yasir Ghunaim, Motasem Alfarra +5
For medical imaging AI models to be clinically impactful, they must generalize. However, this goal is hindered by (i) diverse types of distribution shifts, such as temporal, demogr…
Online Distillation with Continual Learning for Cyclic Domain Shifts
Joachim Houyon, Anthony Cioppa, Yasir Ghunaim +5
In recent years, online distillation has emerged as a powerful technique for adapting real-time deep neural networks on the fly using a slow, but accurate teacher model. However, a…
Real-Time Evaluation in Online Continual Learning: A New Hope
Yasir Ghunaim, Adel Bibi, Kumail Alhamoud +5
Current evaluations of Continual Learning (CL) methods typically assume that there is no constraint on training time and computation. This is an unrealistic assumption for any real…