309 citations · 1.2k across the 38 of their papers we have counts for
5 papers · 1 filter
The PlayStation Reinforcement Learning Environment (PSXLE)
Carlos Purves, Cătălina Cangea, Petar Veličković
We propose a new benchmark environment for evaluating Reinforcement Learning (RL) algorithms: the PlayStation Learning Environment (PSXLE), a PlayStation emulator modified to expos…
Neural Execution of Graph Algorithms
Petar Veličković, Rex Ying, Matilde Padovano +2
Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs. In almost all cases so far, however, they have been applied to di…
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković +2
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is u…
Spatio-Temporal Deep Graph Infomax
Felix L. Opolka, Aaron Solomon, Cătălina Cangea +3
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes ove…
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data
Alexander G. Rakowski, Petar Veličković, Enrico Dall'Ara +1
ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data. Specifically, this pap…