Petar Veličković
Petar Veličković
Staff Research Scientist, Google DeepMind | Affiliated Lecturer, University of Cambridge
Verified email at - Homepage
Cited by
Cited by
Graph Attention Networks
P Veličković, G Cucurull, A Casanova, A Romero, P Liò, Y Bengio
6th International Conference on Learning Representations (ICLR 2018), 2018
Deep Graph Infomax
P Veličković, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm
7th International Conference on Learning Representations (ICLR 2019), 2019
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
MM Bronstein, J Bruna, T Cohen, P Veličković
arXiv preprint arXiv:2104.13478, 2021
Principal neighbourhood aggregation for graph nets
G Corso*, L Cavalleri*, D Beaini, P Liò, P Veličković
Advances in Neural Information Processing Systems 33, 2020
Advancing mathematics by guiding human intuition with AI
A Davies, P Veličković, L Buesing, S Blackwell, D Zheng, N Tomašev, ...
Nature 600 (7887), 70-74, 2021
Towards Sparse Hierarchical Graph Classifiers
C Cangea*, P Veličković*, N Jovanović, T Kipf, P Liò
arXiv preprint arXiv:1811.01287, 2018
Large-Scale Representation Learning on Graphs via Bootstrapping
S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ...
10th International Conference on Learning Representations (ICLR 2022), 2022
Combinatorial optimization and reasoning with graph neural networks.
Q Cappart, D Chételat, EB Khalil, A Lodi, C Morris, P Velickovic
J. Mach. Learn. Res. 24, 130:1-130:61, 2023
Eta prediction with graph neural networks in google maps
A Derrow-Pinion, J She, D Wong, O Lange, T Hester, L Perez, ...
Proceedings of the 30th ACM International Conference on Information …, 2021
Neural Execution of Graph Algorithms
P Veličković, R Ying, M Padovano, R Hadsell, C Blundell
8th International Conference on Learning Representations (ICLR 2020), 2020
Parapred: antibody paratope prediction using convolutional and recurrent neural networks
E Liberis, P Veličković, P Sormanni, M Vendruscolo, P Liò
Bioinformatics 34 (17), 2944-2950, 2018
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
J Godwin, M Schaarschmidt, AL Gaunt, A Sanchez-Gonzalez, ...
10th International Conference on Learning Representations (ICLR 2022), 2022
Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices
A Mathur, T Zhang, S Bhattacharya, P Velickovic, L Joffe, ND Lane, ...
2018 17th ACM/IEEE International Conference on Information Processing in …, 2018
Neural Algorithmic Reasoning
P Veličković, C Blundell
Patterns 2 (7), 2021
Drug-drug adverse effect prediction with graph co-attention
A Deac, YH Huang, P Veličković, P Liò, J Tang
arXiv preprint arXiv:1905.00534, 2019
On the role of planning in model-based deep reinforcement learning
JB Hamrick, AL Friesen, F Behbahani, A Guez, F Viola, S Witherspoon, ...
9th International Conference on Learning Representations (ICLR 2021), 2021
Attentive cross-modal paratope prediction
A Deac, P Veličković, P Sormanni
Journal of Computational Biology, 2018
Pointer graph networks
P Veličković, L Buesing, MC Overlan, R Pascanu, O Vinyals, C Blundell
Advances in Neural Information Processing Systems 33, 2020
X-CNN: Cross-modal convolutional neural networks for sparse datasets
P Veličković, D Wang, ND Lane, P Liò
2016 IEEE symposium series on computational intelligence (SSCI), 1-8, 2016
Message passing all the way up
P Veličković
arXiv preprint arXiv:2202.11097, 2022
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