14 citations · 15 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
FDAPT: Federated Domain-adaptive Pre-training for Language Models
Lekang Jiang, Filip Svoboda, Nicholas D. Lane
Foundation models (FMs) have shown prominent success in a wide range of tasks. Their applicability to specific domain-task pairings relies on the availability of, both, high-qualit…
cs.LG2023★ 14 cited
A Federated Learning Benchmark for Drug-Target Interaction
Gianluca Mittone, Filip Svoboda, Marco Aldinucci +2
Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to reg…