104 citations · 146 across the 5 of their papers we have counts for
6 papers
FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design
Asal Mehradfar, Xuzhe Zhao, Yilun Huang +5
Designing analog circuits from performance specifications is a complex, multi-stage process encompassing topology selection, parameter inference, and layout feasibility. We introdu…
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling
Emir Ceyani, Han Xie, Baturalp Buyukates +2
Graphs are crucial for modeling relational and biological data. As datasets grow larger in real-world scenarios, the risk of exposing sensitive information increases, making privac…
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
Onat Dalmaz, Usama Mirza, Gökberk Elmas +5
Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images. To facilitate collaboration, federate…
Federated Learning of Generative Image Priors for MRI Reconstruction
Gokberk Elmas, Salman UH Dar, Yilmaz Korkmaz +5
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL)…
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
Chaoyang He, Emir Ceyani, Keshav Balasubramanian +2
Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-str…
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani +11
Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a ma…