69 citations · 119 across the 8 of their papers we have counts for
11 papers
Predicting Patient Outcomes with Graph Representation Learning
Emma Rocheteau, Catherine Tong, Petar Veličković +2
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused heavily on the physiological time series data, largely ignoring sparse data such as diagnose…
A step towards neural genome assembly
Lovro Vrček, Petar Veličković, Mile Šikić
De novo genome assembly focuses on finding connections between a vast amount of short sequences in order to reconstruct the original genome. The central problem of genome assembly…
On the role of planning in model-based deep reinforcement learning
Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani +7
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learn…
Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini +2
Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data. Recent work on their expressive power has focused on is…
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…