10 citations · 11 across the 4 of their papers we have counts for
4 papers
GATTA: Graph Active Learning with Test-Time Augmentation
Zsombor Bánfi, András Gézsi, András Formanek
Test-time augmentation (TTA) has proven effective for improving model robustness and uncertainty estimation in computer vision, yet its application to graph-structured data remains…
A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset
Andrs Formanek, Anna Vincze, Richrd Bicsak +3
We present a unique, multitask dataset comprising 143 drug and drug candidate molecules, each evaluated on in vitro, parallel artificial-membrane permeability assays (PAMPA) using…
BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Framework
András Millinghoffer, András Formanek, András Antos +1
The challenge of effectively transferring knowledge across multiple tasks is of critical importance and is also present in downstream tasks with foundation models. However, the nat…
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Martijn Oldenhof, Gergely Ács, Balázs Pejó +44
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), whic…