Petar Velickovic

According to our database, Petar Velickovic authored at least 78 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Categorical Deep Learning: An Algebraic Theory of Architectures.

Bruno Gavranovic, Paul Lessard, Andrew Dudzik, Tamara von Glehn, João G. M. Araújo, Petar Velickovic

CoRR, 2024

Position Paper: Challenges and Opportunities in Topological Deep Learning.

Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

CoRR, 2024

2023

Time-warping invariant quantum recurrent neural networks via quantum-classical adaptive gating.

Ivana Nikoloska, Osvaldo Simeone, Leonardo Banchi, Petar Velickovic

Mach. Learn. Sci. Technol., December, 2023

Scientific discovery in the age of artificial intelligence.

Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik

Nat., 2023

Combinatorial Optimization and Reasoning with Graph Neural Networks.

Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic

J. Mach. Learn. Res., 2023

Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search.

Abbas Mehrabian, Ankit Anand, Hyunjik Kim, Nicolas Sonnerat, Matej Balog, Gheorghe Comanici, Tudor Berariu, Andrew Lee, Anian Ruoss, Anna Bulanova, Daniel Toyama, Sam Blackwell, Bernardino Romera-Paredes, Petar Velickovic, Laurent Orseau, Joonkyung Lee, Anurag Murty Naredla, Doina Precup, Adam Zsolt Wagner

CoRR, 2023

TacticAI: an AI assistant for football tactics.

Zhe Wang, Petar Velickovic, Daniel Hennes, Nenad Tomasev, Laurel Prince, Michael Kaisers, Yoram Bachrach, Romuald Elie, Li Kevin Wenliang, Federico Piccinini, William Spearman, Ian Graham, Jerome T. Connor, Yi Yang, Adrià Recasens, Mina Khan, Nathalie Beauguerlange, Pablo Sprechmann, Pol Moreno, Nicolas Heess, Michael Bowling, Demis Hassabis, Karl Tuyls

CoRR, 2023

Geometric Epitope and Paratope Prediction.

Marco Pegoraro, Clémentine Dominé, Emanuele Rodolà, Petar Velickovic, Andreea Deac

CoRR, 2023

Neural Priority Queues for Graph Neural Networks.

Rishabh Jain, Petar Velickovic, Pietro Liò

CoRR, 2023

Latent Space Representations of Neural Algorithmic Reasoners.

Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic

CoRR, 2023

Parallel Algorithms Align with Neural Execution.

Valerie Engelmayer, Dobrik Georgiev, Petar Velickovic

CoRR, 2023

Recursive Algorithmic Reasoning.

Dulhan Hansaja Jayalath, Jonas Jürß, Petar Velickovic

CoRR, 2023

Asynchronous Algorithmic Alignment with Cocycles.

Andrew Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic

CoRR, 2023

How does over-squashing affect the power of GNNs?

Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic

CoRR, 2023

Everything is Connected: Graph Neural Networks.

Petar Velickovic

CoRR, 2023

Affinity-Aware Graph Networks.

Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Velickovic, Sreenivas Gollapudi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Algorithmic Reasoning with Causal Regularisation.

Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic

Proceedings of the International Conference on Machine Learning, 2023

Half-Hop: A graph upsampling approach for slowing down message passing.

Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer

Proceedings of the International Conference on Machine Learning, 2023

Dual Algorithmic Reasoning.

Danilo Numeroso, Davide Bacciu, Petar Velickovic

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Learning heuristics for A.

Danilo Numeroso, Davide Bacciu, Petar Velickovic

CoRR, 2022

Message passing all the way up.

Petar Velickovic

CoRR, 2022

Sheaf Neural Networks with Connection Laplacians.

Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò

Proceedings of the Topological, 2022

Graph Neural Networks are Dynamic Programmers.

Andrew Joseph Dudzik, Petar Velickovic

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning to Configure Computer Networks with Neural Algorithmic Reasoning.

Luca Beurer-Kellner, Martin T. Vechev, Laurent Vanbever, Petar Velickovic

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Utility of Equivariant Message Passing in Cortical Mesh Segmentation.

Dániel Unyi, Ferdinando Insalata, Petar Velickovic, Bálint Gyires-Tóth

Proceedings of the Medical Image Understanding and Analysis - 26th Annual Conference, 2022

Reasoning-Modulated Representations.

Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell

Proceedings of the Learning on Graphs Conference, 2022

Learning Graph Search Heuristics.

Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Velickovic, Zhitao Ying, Jure Leskovec, Pietro Liò

Proceedings of the Learning on Graphs Conference, 2022

Learnable Commutative Monoids for Graph Neural Networks.

Euan Ong, Petar Velickovic

Proceedings of the Learning on Graphs Conference, 2022

A Generalist Neural Algorithmic Learner.

Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bosnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Velickovic

Proceedings of the Learning on Graphs Conference, 2022

Continuous Neural Algorithmic Planners.

Yu He, Petar Velickovic, Pietro Liò, Andreea Deac

Proceedings of the Learning on Graphs Conference, 2022

Expander Graph Propagation.

Andreea Deac, Marc Lackenby, Petar Velickovic

Proceedings of the Learning on Graphs Conference, 2022

The CLRS Algorithmic Reasoning Benchmark.

Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell

Proceedings of the International Conference on Machine Learning, 2022

Large-Scale Representation Learning on Graphs via Bootstrapping.

Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko

Proceedings of the Tenth International Conference on Learning Representations, 2022

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

Proceedings of the Tenth International Conference on Learning Representations, 2022

Algorithmic Concept-Based Explainable Reasoning.

Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan, Petar Velickovic, Pietro Lió

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Neural algorithmic reasoning.

Petar Velickovic, Charles Blundell

Patterns, 2021

Advancing mathematics by guiding human intuition with AI.

Alex Davies, Petar Velickovic, Lars Buesing, Sam Blackwell, Daniel Zheng, Nenad Tomasev, Richard Tanburn, Peter W. Battaglia, Charles Blundell, András Juhász, Marc Lackenby, Geordie Williamson, Demis Hassabis, Pushmeet Kohli

Nat., 2021

Relating Graph Neural Networks to Structural Causal Models.

Matej Zecevic, Devendra Singh Dhami, Petar Velickovic, Kristian Kersting

CoRR, 2021

Large-scale graph representation learning with very deep GNNs and self-supervision.

Ravichandra Addanki, Peter W. Battaglia, David Budden, Andreea Deac, Jonathan Godwin, Thomas Keck, Wai Lok Sibon Li, Alvaro Sanchez-Gonzalez, Jacklynn Stott, Shantanu Thakoor, Petar Velickovic

CoRR, 2021

Very Deep Graph Neural Networks Via Noise Regularisation.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

CoRR, 2021

Neural message passing for joint paratope-epitope prediction.

Alice Del Vecchio, Andreea Deac, Pietro Liò, Petar Velickovic

CoRR, 2021

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic

CoRR, 2021

Persistent Message Passing.

Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell, Petar Velickovic

CoRR, 2021

Bootstrapped Representation Learning on Graphs.

Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko

CoRR, 2021

Predicting Patient Outcomes with Graph Representation Learning.

Emma Rocheteau, Catherine Tong, Petar Velickovic, Nicholas D. Lane, Pietro Liò

CoRR, 2021

How to transfer algorithmic reasoning knowledge to learn new algorithms?

Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Algorithmic Reasoners are Implicit Planners.

Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Distance Embeddings for Biological Sequences.

Gabriele Corso, Zhitao Ying, Michal Pándy, Petar Velickovic, Jure Leskovec, Pietro Liò

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the role of planning in model-based deep reinforcement learning.

Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Velickovic, Theophane Weber

Proceedings of the 9th International Conference on Learning Representations, 2021

ETA Prediction with Graph Neural Networks in Google Maps.

Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Velickovic

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification.

Catalina Cangea, Petar Velickovic, Pietro Liò

IEEE Trans. Neural Networks Learn. Syst., 2020

A step towards neural genome assembly.

Lovro Vrcek, Petar Velickovic, Mile Sikic

CoRR, 2020

XLVIN: eXecuted Latent Value Iteration Nets.

Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic

CoRR, 2020

Hierachial Protein Function Prediction with Tails-GNNs.

Stefan Spalevic, Petar Velickovic, Jovana J. Kovacevic, Mladen Nikolic

CoRR, 2020

Pointer Graph Networks.

Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Principal Neighbourhood Aggregation for Graph Nets.

Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Execution of Graph Algorithms.

Petar Velickovic, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

The resurgence of structure in deep neural networks

Petar Velickovic

PhD thesis, 2019

Attentive Cross-Modal Paratope Prediction.

Andreea Deac, Petar Velickovic, Pietro Sormanni

J. Comput. Biol., 2019

The PlayStation Reinforcement Learning Environment (PSXLE).

Carlos Purves, Catalina Cangea, Petar Velickovic

CoRR, 2019

Drug-Drug Adverse Effect Prediction with Graph Co-Attention.

Andreea Deac, Yu-Hsiang Huang, Petar Velickovic, Pietro Liò, Jian Tang

CoRR, 2019

Spatio-Temporal Deep Graph Infomax.

Felix L. Opolka, Aaron Solomon, Catalina Cangea, Petar Velickovic, Pietro Liò, R. Devon Hjelm

CoRR, 2019

ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data.

Alexander G. Rakowski, Petar Velickovic, Enrico Dall'Ara, Pietro Liò

CoRR, 2019

Deep Graph Infomax.

Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Towards Sparse Hierarchical Graph Classifiers.

Catalina Cangea, Petar Velickovic, Nikola Jovanovic, Thomas Kipf, Pietro Liò

CoRR, 2018

Parapred: antibody paratope prediction using convolutional and recurrent neural networks.

Edgar Liberis, Petar Velickovic, Pietro Sormanni, Michele Vendruscolo, Pietro Liò

Bioinform., 2018

Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data.

Petar Velickovic, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville

Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2018

Automatic Inference of Cross-Modal Connection Topologies for X-CNNs.

Laurynas Karazija, Petar Velickovic, Pietro Liò

Proceedings of the Advances in Neural Networks - ISNN 2018, 2018

Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices.

Akhil Mathur, Tianlin Zhang, Sourav Bhattacharya, Petar Velickovic, Leonid Joffe, Nicholas D. Lane, Fahim Kawsar, Pietro Liò

Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2018

Graph Attention Networks.

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio

Proceedings of the 6th International Conference on Learning Representations, 2018

Multi-omics data integration using cross-modal neural networks.

Ioana Bica, Petar Velickovic, Hui Xiao

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017

Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders.

Momchil Peychev, Petar Velickovic, Pietro Liò

CoRR, 2017

Cross-modal Recurrent Models for Human Weight Objective Prediction from Multimodal Time-series Data.

Petar Velickovic, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville

CoRR, 2017

XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification.

Catalina Cangea, Petar Velickovic, Pietro Liò

CoRR, 2017

Scaling health analytics to millions without compromising privacy using deep distributed behavior models.

Petar Velickovic, Nicholas D. Lane, Sourav Bhattacharya, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville

Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2017

2016

Molecular multiplex network inference using Gaussian mixture hidden Markov models.

Petar Velickovic, Pietro Liò

J. Complex Networks, 2016

Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types.

Petar Velickovic, Pietro Liò

Bioinform., 2016

X-CNN: Cross-modal convolutional neural networks for sparse datasets.

Petar Velickovic, Duo Wang, Nicholas D. Lane, Pietro Liò

Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016