1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks
Daniel Manu, Abee Alazzwi
Generative adversarial networks (GANs) have garnered considerable attention in molecular discovery for their ability to generate novel and high-quality molecules. To efficiently tr…
cs.LG2023★ 1 cited
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery
Daniel Manu, Jingjing Yao, Wuji Liu +1
Recent advances in deep learning have accelerated its use in various applications, such as cellular image analysis and molecular discovery. In molecular discovery, a generative adv…