
Petar Veličković
Petar Veličković is a Research Scientist at DeepMind. He holds a PhD degree from the University of Cambridge (obtained under the supervision of Pietro Liò), with prior collaborations at Nokia Bell Labs and Mila. His current research interests broadly involve devising neural network architectures that operate on nontrivially structured data (such as graphs), and their applications in algorithmic reasoning and computational biology. He has published his work in these areas at both machine learning venues (ICLR, NeurIPS-W, ICML-W) and biomedical venues and journals (Bioinformatics, PLOS One, JCB, PervasiveHealth). In particular, he is the first author of Graph Attention Networks, a popular convolutional layer for graphs, and Deep Graph Infomax, a scalable local/global unsupervised learning pipeline for graphs. His research has been featured in media outlets such as ZDNet. Additionally, he has co-organised workshops on Graph Representation Learning at ICLR 2019 and NeurIPS 2019.
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Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso
Luca Cavalleri
Dominique Beaini
Pietro Liò
Neural Information Processing Systems (2020) (to appear)
Pointer Graph Networks
Matthew C. Overlan
Razvan Pascanu
Charles Blundell
Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020) (2020) (to appear)
Neural Execution of Graph Algorithms
Rex Ying
Matilde Padovano
Raia Hadsell
Charles Blundell
International Conference on Learning Representations (2020)
Deep Graph Infomax
Wiliam Fedus
William L. Hamilton
Pietro Liò
Yoshua Bengio
R Devon Hjelm
International Conference on Learning Representations (2019)