7 citations · 21 across the 26 of their papers we have counts for
8 papers · 1 filter
Knowledge Graph Sparsification for GNN-based Rare Disease Diagnosis
Premt Cara, Kamilia Zaripova, David Bani-Harouni +2
Rare genetic disease diagnosis faces critical challenges: insufficient patient data, inaccessible full genome sequencing, and the immense number of possible causative genes. These…
Stress-Aware Resilient Neural Training
Ashkan Shakarami, Yousef Yeganeh, Azade Farshad +3
This paper introduces Stress-Aware Learning, a resilient neural training paradigm in which deep neural networks dynamically adjust their optimization behavior - whether under stabl…
PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone
Kamilia Zaripova, Ege Özsoy, Nassir Navab +1
Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic di…
VeLU: Variance-enhanced Learning Unit for Deep Neural Networks
Ashkan Shakarami, Yousef Yeganeh, Azade Farshad +3
Activation functions play a critical role in deep neural networks by shaping gradient flow, optimization stability, and generalization. While ReLU remains widely used due to its si…
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation
Pavel Jahoda, Azade Farshad, Yousef Yeganeh +2
Scene graphs have emerged as accurate descriptive priors for image generation and manipulation tasks, however, their complexity and diversity of the shapes and relations of objects…
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data
Yousef Yeganeh, Azade Farshad, Johann Boschmann +3
Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patien…