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ORCIDPetar Velickovic
Petar Veličković
Person information
- unicode name: Petar Veličković
- affiliation: Google DeepMind, London, UK
- affiliation (former, PhD): University of Cambridge, Department of Computer Science and Technology, Cambridge, UK
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2020 – today
- 2026
[i96]Petar Velickovic, Federico Barbero, Christos Perivolaropoulos, Simon Osindero, Razvan Pascanu:
Perplexity Cannot Always Tell Right from Wrong. CoRR abs/2601.22950 (2026)- [i95]Alex Vitvitskyi, Michael Boratko, Matej Grcic, Razvan Pascanu, Deep Shah, Petar Velickovic:
Mining Generalizable Activation Functions. CoRR abs/2602.05688 (2026) - [i94]Dharshan Kumaran, Arthur Conmy, Federico Barbero, Simon Osindero, Viorica Patraucean, Petar Velickovic:
How do LLMs Compute Verbal Confidence. CoRR abs/2603.17839 (2026) - [i93]Dharshan Kumaran, Nathaniel Daw, Simon Osindero, Petar Velickovic, Viorica Patraucean:
Causal Evidence that Language Models use Confidence to Drive Behavior. CoRR abs/2603.22161 (2026) - [i92]Xiangming Gu, Soham De, Larisa Markeeva, Petar Velickovic, Razvan Pascanu:
Understanding Performance Gap Between Parallel and Sequential Sampling in Large Reasoning Models. CoRR abs/2604.05868 (2026) - [i91]Xiangming Gu, Soham De, Michalis K. Titsias, Larisa Markeeva, Petar Velickovic, Razvan Pascanu:
The Illusion of Stochasticity in LLMs. CoRR abs/2604.06543 (2026) - [i90]Dharshan Kumaran, Viorica Patraucean, Simon Osindero, Petar Velickovic, Nathaniel Daw:
How LLMs Detect and Correct Their Own Errors: The Role of Internal Confidence Signals. CoRR abs/2604.22271 (2026) - [i89]Dharshan Kumaran, Viorica Patraucean, Maks Ovsjanikov, Petar Velickovic, Nathaniel Daw:
The Computational Basis of Confidence in Large Language Models. CoRR abs/2607.12447 (2026) - [i88]Ben Finkelshtein, André Linhares, Petar Velickovic, Bryan Perozzi, Mikhail Galkin:
The Universal Classifier for Graph Learning. CoRR abs/2609.36302 (2026) - 2025
- [c52]Federico Barbero, Alex Vitvitskyi, Christos Perivolaropoulos, Razvan Pascanu, Petar Velickovic:
Round and Round We Go! What makes Rotary Positional Encodings useful? ICLR 2025 - [c51]Artur Back de Luca, George Giapitzakis, Shenghao Yang, Petar Velickovic, Kimon Fountoulakis:
Positional Attention: Expressivity and Learnability of Algorithmic Computation. ICML 2025 - [c50]John Schultz, Jakub Adámek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada A. Lewis, Anian Ruoss, Tom Zahavy, Petar Velickovic, Laurel Prince, Satinder Singh, Eric Malmi, Nenad Tomasev:
Mastering Board Games by External and Internal Planning with Language Models. ICML 2025 - [c49]Petar Velickovic, Christos Perivolaropoulos, Federico Barbero, Razvan Pascanu:
Softmax is not Enough (for Sharp Size Generalisation). ICML 2025 - [c48]Alex Vitvitskyi, João Guilherme Madeira Araújo, Marc Lackenby, Petar Velickovic:
What Makes a Good Feedforward Computational Graph? ICML 2025 - [i87]Alex Vitvitskyi, João G. M. Araújo, Marc Lackenby, Petar Velickovic:
What makes a good feedforward computational graph? CoRR abs/2502.06751 (2025) - [i86]Federico Barbero, Alvaro Arroyo, Xiangming Gu, Christos Perivolaropoulos, Michael M. Bronstein, Petar Velickovic, Razvan Pascanu:
Why do LLMs attend to the first token? CoRR abs/2504.02732 (2025) - [i85]Dharshan Kumaran, Stephen M. Fleming, Larisa Markeeva, Joe Heyward, Andrea Banino, Mrinal Mathur, Razvan Pascanu, Simon Osindero, Benedetto De Martino, Petar Velickovic, Viorica Patraucean:
How Overconfidence in Initial Choices and Underconfidence Under Criticism Modulate Change of Mind in Large Language Models. CoRR abs/2507.03120 (2025) - [i84]Owen Lewis, Neil Ghani, Andrew Dudzik, Christos Perivolaropoulos, Razvan Pascanu, Petar Velickovic:
Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists. CoRR abs/2507.08796 (2025) - [i83]Razvan Pascanu, Clare Lyle, Ionut-Vlad Modoranu, Naima Elosegui Borras, Dan Alistarh, Petar Velickovic, Sarath Chandar, Soham De, James Martens:
Optimizers Qualitatively Alter Solutions And We Should Leverage This. CoRR abs/2507.12224 (2025) - [i82]Stjepan Pozgaj, Dobrik Georgiev, Marin Silic, Petar Velickovic:
KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems. CoRR abs/2509.15239 (2025) - [i81]Isaac Reid, Arijit Sehanobish, Cederik Höfs, Bruno Mlodozeniec, Leonhard Vulpius, Federico Barbero, Adrian Weller, Krzysztof Choromanski, Richard E. Turner, Petar Velickovic:
Wavelet-Induced Rotary Encodings: RoPE Meets Graphs. CoRR abs/2509.22259 (2025) - [i80]Federico Barbero, Xiangming Gu, Christopher A. Choquette-Choo, Chawin Sitawarin, Matthew Jagielski, Itay Yona, Petar Velickovic, Ilia Shumailov, Jamie Hayes:
Extracting alignment data in open models. CoRR abs/2510.18554 (2025) - [i79]Jason Wu, Petar Velickovic:
Leveraging Classical Algorithms for Graph Neural Networks. CoRR abs/2510.21574 (2025) - 2024
- [j12]Marco Pegoraro
, Clémentine C. J. Dominé, Emanuele Rodolà, Petar Velickovic, Andreea Deac:
Geometric epitope and paratope prediction. Bioinform. 40(6) (2024) - [j11]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? Trans. Mach. Learn. Res. 2024 (2024) - [c47]Bao Nguyen, Anjana Yodaiken, Petar Velickovic:
Alignment of MPNNs and Graph Transformers. GRaM 2024: 35-49 - [c46]Pablo Monteagudo-Lago, Arielle Rosinski, Andrew Joseph Dudzik, Petar Velickovic:
Asynchrony Invariance Loss Functions for Graph Neural Networks. GRaM 2024: 65-77 - [c45]Igor Sterner, Shiye Su, Petar Velickovic:
Commute-Time-Optimised Graphs for GNNs. GRaM 2024: 103-112 - [c44]Bruno Gavranovic, Paul Lessard, Andrew Joseph Dudzik, Tamara von Glehn, João Guilherme Madeira Araújo, Petar Velickovic:
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures. ICML 2024: 15209-15241 - [c43]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, 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:
Position: Topological Deep Learning is the New Frontier for Relational Learning. ICML 2024: 39529-39555 - [c42]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:
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search. IJCAI 2024: 6985-6993 - [c41]JJ Wilson, Maya Bechler-Speicher, Petar Velickovic:
Cayley Graph Propagation. LoG 2024: 8 - [c40]Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João Guilherme Madeira Araújo, Oleksandr Vitvitskyi, Razvan Pascanu, Petar Velickovic:
Transformers need glasses! Information over-squashing in language tasks. NeurIPS 2024 - [i78]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. 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:
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR abs/2402.08871 (2024) - [i77]Bruno Gavranovic, Paul Lessard, Andrew Dudzik, Tamara von Glehn, João G. M. Araújo, Petar Velickovic:
Categorical Deep Learning: An Algebraic Theory of Architectures. CoRR abs/2402.15332 (2024) - [i76]Katarina Petrovic, Shenyang Huang, Farimah Poursafaei, Petar Velickovic:
Temporal Graph Rewiring with Expander Graphs. CoRR abs/2406.02362 (2024) - [i75]Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, Petar Velickovic:
The CLRS-Text Algorithmic Reasoning Language Benchmark. CoRR abs/2406.04229 (2024) - [i74]Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan Kumaran, João G. M. Araújo, Alex Vitvitskyi, Razvan Pascanu, Petar Velickovic:
Transformers need glasses! Information over-squashing in language tasks. CoRR abs/2406.04267 (2024) - [i73]Wilfried Bounsi, Borja Ibarz, Andrew Dudzik, Jessica B. Hamrick, Larisa Markeeva, Alex Vitvitskyi, Razvan Pascanu, Petar Velickovic:
Transformers meet Neural Algorithmic Reasoners. CoRR abs/2406.09308 (2024) - [i72]Igor Sterner, Shiye Su, Petar Velickovic:
Commute-Time-Optimised Graphs for GNNs. CoRR abs/2407.08762 (2024) - [i71]Kaijia Xu, Petar Velickovic:
Recurrent Aggregators in Neural Algorithmic Reasoning. CoRR abs/2409.07154 (2024) - [i70]Petar Velickovic, Christos Perivolaropoulos, Federico Barbero, Razvan Pascanu:
softmax is not enough (for sharp out-of-distribution). CoRR abs/2410.01104 (2024) - [i69]Artur Back de Luca, George Giapitzakis, Shenghao Yang, Petar Velickovic, Kimon Fountoulakis:
Positional Attention: Out-of-Distribution Generalization and Expressivity for Neural Algorithmic Reasoning. CoRR abs/2410.01686 (2024) - [i68]JJ Wilson, Maya Bechler-Speicher, Petar Velickovic:
Cayley Graph Propagation. CoRR abs/2410.03424 (2024) - [i67]Federico Barbero, Alex Vitvitskyi, Christos Perivolaropoulos, Razvan Pascanu, Petar Velickovic:
Round and Round We Go! What makes Rotary Positional Encodings useful? CoRR abs/2410.06205 (2024) - [i66]Efimia Panagiotaki, Daniele De Martini, Lars Kunze, Petar Velickovic:
NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms. CoRR abs/2410.11031 (2024) - [i65]Petar Velickovic, Alex Vitvitskyi, Larisa Markeeva, Borja Ibarz, Lars Buesing, Matej Balog, Alexander Novikov:
Amplifying human performance in combinatorial competitive programming. CoRR abs/2411.19744 (2024) - [i64]John Schultz, Jakub Adámek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada A. Lewis, Anian Ruoss, Tom Zahavy, Petar Velickovic, Laurel Prince, Satinder Singh, Eric Malmi, Nenad Tomasev:
Mastering Board Games by External and Internal Planning with Language Models. CoRR abs/2412.12119 (2024) - 2023
- [j10]Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic:
Combinatorial Optimization and Reasoning with Graph Neural Networks. J. Mach. Learn. Res. 24: 130:1-130:61 (2023) - [j9]Ivana Nikoloska, Osvaldo Simeone, Leonardo Banchi, Petar Velickovic:
Time-warping invariant quantum recurrent neural networks via quantum-classical adaptive gating. Mach. Learn. Sci. Technol. 4(4): 45038 (2023) - [j8]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 K. 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:
Scientific discovery in the age of artificial intelligence. Nat. 620(7972): 47-60 (2023) - [c39]Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Dual Algorithmic Reasoning. ICLR 2023 - [c38]Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer:
Half-Hop: A graph upsampling approach for slowing down message passing. ICML 2023: 1341-1360 - [c37]Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic:
Neural Algorithmic Reasoning with Causal Regularisation. ICML 2023: 2272-2288 - [c36]Andrew Joseph Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic:
Asynchronous Algorithmic Alignment With Cocycles. LoG 2023: 3 - [c35]Jonas Jürß, Dulhan Hansaja Jayalath, Petar Velickovic:
Recursive Algorithmic Reasoning. LoG 2023: 5 - [c34]Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic:
Latent Space Representations of Neural Algorithmic Reasoners. LoG 2023: 10 - [c33]Valerie Engelmayer, Dobrik Georgiev, Petar Velickovic:
Parallel Algorithms Align With Neural Execution. LoG 2023: 31 - [c32]Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Velickovic, Sreenivas Gollapudi:
Affinity-Aware Graph Networks. NeurIPS 2023 - [i63]Ivana Nikoloska, Osvaldo Simeone, Leonardo Banchi, Petar Velickovic:
Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating. CoRR abs/2301.08173 (2023) - [i62]Petar Velickovic:
Everything is Connected: Graph Neural Networks. CoRR abs/2301.08210 (2023) - [i61]Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Dual Algorithmic Reasoning. CoRR abs/2302.04496 (2023) - [i60]Beatrice Bevilacqua, Kyriacos Nikiforou, Borja Ibarz, Ioana Bica, Michela Paganini, Charles Blundell, Jovana Mitrovic, Petar Velickovic:
Neural Algorithmic Reasoning with Causal Regularisation. CoRR abs/2302.10258 (2023) - [i59]Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? CoRR abs/2306.03589 (2023) - [i58]Andrew Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic:
Asynchronous Algorithmic Alignment with Cocycles. CoRR abs/2306.15632 (2023) - [i57]Dulhan Hansaja Jayalath, Jonas Jürß, Petar Velickovic:
Recursive Algorithmic Reasoning. CoRR abs/2307.00337 (2023) - [i56]Valerie Engelmayer, Dobrik Georgiev, Petar Velickovic:
Parallel Algorithms Align with Neural Execution. CoRR abs/2307.04049 (2023) - [i55]Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic:
Latent Space Representations of Neural Algorithmic Reasoners. CoRR abs/2307.08874 (2023) - [i54]Rishabh Jain, Petar Velickovic, Pietro Liò:
Neural Priority Queues for Graph Neural Networks. CoRR abs/2307.09660 (2023) - [i53]Marco Pegoraro, Clémentine C. J. Dominé, Emanuele Rodolà, Petar Velickovic, Andreea Deac:
Geometric Epitope and Paratope Prediction. CoRR abs/2307.13608 (2023) - [i52]Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer:
Half-Hop: A graph upsampling approach for slowing down message passing. CoRR abs/2308.09198 (2023) - [i51]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:
TacticAI: an AI assistant for football tactics. CoRR abs/2310.10553 (2023) - [i50]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:
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search. CoRR abs/2311.03583 (2023) - 2022
- [c31]Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan, Petar Velickovic, Pietro Lió:
Algorithmic Concept-Based Explainable Reasoning. AAAI 2022: 6685-6693 - [c30]Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia:
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. ICLR 2022 - [c29]Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko:
Large-Scale Representation Learning on Graphs via Bootstrapping. ICLR 2022 - [c28]Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell:
The CLRS Algorithmic Reasoning Benchmark. ICML 2022: 22084-22102 - [c27]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:
A Generalist Neural Algorithmic Learner. LoG 2022: 2 - [c26]Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Velickovic, Zhitao Ying, Jure Leskovec, Pietro Liò:
Learning Graph Search Heuristics. LoG 2022: 10 - [c25]Andreea Deac, Marc Lackenby, Petar Velickovic:
Expander Graph Propagation. LoG 2022: 38 - [c24]Euan Ong, Petar Velickovic:
Learnable Commutative Monoids for Graph Neural Networks. LoG 2022: 43 - [c23]Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell:
Reasoning-Modulated Representations. LoG 2022: 50 - [c22]Yu He, Petar Velickovic, Pietro Liò, Andreea Deac:
Continuous Neural Algorithmic Planners. LoG 2022: 54 - [c21]Dániel Unyi, Ferdinando Insalata, Petar Velickovic, Bálint Gyires-Tóth:
Utility of Equivariant Message Passing in Cortical Mesh Segmentation. MIUA 2022: 412-424 - [c20]Luca Beurer-Kellner, Martin T. Vechev, Laurent Vanbever, Petar Velickovic:
Learning to Configure Computer Networks with Neural Algorithmic Reasoning. NeurIPS 2022 - [c19]Andrew Joseph Dudzik, Petar Velickovic:
Graph Neural Networks are Dynamic Programmers. NeurIPS 2022 - [c18]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. TAG-ML 2022: 28-36 - [i49]Petar Velickovic:
Message passing all the way up. CoRR abs/2202.11097 (2022) - [i48]Andrew Dudzik, Petar Velickovic:
Graph Neural Networks are Dynamic Programmers. CoRR abs/2203.15544 (2022) - [i47]Danilo Numeroso, Davide Bacciu, Petar Velickovic:
Learning heuristics for A. CoRR abs/2204.08938 (2022) - [i46]Petar Velickovic, Adrià Puigdomènech Badia, David Budden, Razvan Pascanu, Andrea Banino, Misha Dashevskiy, Raia Hadsell, Charles Blundell:
The CLRS Algorithmic Reasoning Benchmark. CoRR abs/2205.15659 (2022) - [i45]Dániel Unyi, Ferdinando Insalata, Petar Velickovic, Bálint Gyires-Tóth:
Utility of Equivariant Message Passing in Cortical Mesh Segmentation. CoRR abs/2206.03164 (2022) - [i44]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. CoRR abs/2206.08702 (2022) - [i43]Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Velickovic, Sreenivas Gollapudi:
Affinity-Aware Graph Networks. CoRR abs/2206.11941 (2022) - [i42]Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bosnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Velickovic:
A Generalist Neural Algorithmic Learner. CoRR abs/2209.11142 (2022) - [i41]Andreea Deac, Marc Lackenby, Petar Velickovic:
Expander Graph Propagation. CoRR abs/2210.02997 (2022) - [i40]Luca Beurer-Kellner, Martin T. Vechev, Laurent Vanbever, Petar Velickovic:
Learning to Configure Computer Networks with Neural Algorithmic Reasoning. CoRR abs/2211.01980 (2022) - [i39]Yu He, Petar Velickovic, Pietro Liò, Andreea Deac:
Continuous Neural Algorithmic Planners. CoRR abs/2211.15839 (2022) - [i38]Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Velickovic, Rex Ying, Jure Leskovec, Pietro Liò:
Learning Graph Search Heuristics. CoRR abs/2212.03978 (2022) - [i37]Euan Ong, Petar Velickovic:
Learnable Commutative Monoids for Graph Neural Networks. CoRR abs/2212.08541 (2022) - 2021
- [j7]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:
Advancing mathematics by guiding human intuition with AI. Nat. 600(7887): 70-74 (2021) - [j6]Petar Velickovic, Charles Blundell:
Neural algorithmic reasoning. Patterns 2(7): 100273 (2021) - [c17]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:
ETA Prediction with Graph Neural Networks in Google Maps. CIKM 2021: 3767-3776 - [c16]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:
On the role of planning in model-based deep reinforcement learning. ICLR 2021 - [c15]Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic:
Combinatorial Optimization and Reasoning with Graph Neural Networks. IJCAI 2021: 4348-4355 - [c14]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. NeurIPS 2021: 15529-15542 - [c13]Gabriele Corso, Zhitao Ying, Michal Pándy, Petar Velickovic, Jure Leskovec, Pietro Liò:
Neural Distance Embeddings for Biological Sequences. NeurIPS 2021: 18539-18551 - [c12]Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, Jian Tang:
How to transfer algorithmic reasoning knowledge to learn new algorithms? NeurIPS 2021: 19500-19512 - [i36]Emma Rocheteau, Catherine Tong, Petar Velickovic, Nicholas D. Lane, Pietro Liò:
Predicting Patient Outcomes with Graph Representation Learning. CoRR abs/2101.03940 (2021) - [i35]Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko:
Bootstrapped Representation Learning on Graphs. CoRR abs/2102.06514 (2021) - [i34]Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic:
Combinatorial optimization and reasoning with graph neural networks. CoRR abs/2102.09544 (2021) - [i33]Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell, Petar Velickovic:
Persistent Message Passing. CoRR abs/2103.01043 (2021) - [i32]Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic:
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. CoRR abs/2104.13478 (2021) - [i31]Petar Velickovic, Charles Blundell:
Neural Algorithmic Reasoning. CoRR abs/2105.02761 (2021) - [i30]Alice Del Vecchio, Andreea Deac, Pietro Liò, Petar Velickovic:
Neural message passing for joint paratope-epitope prediction. CoRR abs/2106.00757 (2021) - [i29]Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia:
Very Deep Graph Neural Networks Via Noise Regularisation. CoRR abs/2106.07971 (2021) - [i28]Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan, Petar Velickovic, Pietro Liò:
Algorithmic Concept-based Explainable Reasoning. CoRR abs/2107.07493 (2021) - [i27]Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell:
Reasoning-Modulated Representations. CoRR abs/2107.08881 (2021) - [i26]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:
Large-scale graph representation learning with very deep GNNs and self-supervision. CoRR abs/2107.09422 (2021) - [i25]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:
ETA Prediction with Graph Neural Networks in Google Maps. CoRR abs/2108.11482 (2021) - [i24]Matej Zecevic, Devendra Singh Dhami, Petar Velickovic, Kristian Kersting:
Relating Graph Neural Networks to Structural Causal Models. CoRR abs/2109.04173 (2021) - [i23]Gabriele Corso, Rex Ying, Michal Pándy, Petar Velickovic, Jure Leskovec, Pietro Liò:
Neural Distance Embeddings for Biological Sequences. CoRR abs/2109.09740 (2021) - [i22]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
Neural Algorithmic Reasoners are Implicit Planners. CoRR abs/2110.05442 (2021) - [i21]Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic, Jian Tang:
How to transfer algorithmic reasoning knowledge to learn new algorithms? CoRR abs/2110.14056 (2021) - 2020
- [j5]Catalina Cangea, Petar Velickovic, Pietro Liò:
XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3711-3720 (2020) - [c11]Petar Velickovic, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell:
Neural Execution of Graph Algorithms. ICLR 2020 - [c10]Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic:
Principal Neighbourhood Aggregation for Graph Nets. NeurIPS 2020 - [c9]Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. NeurIPS 2020 - [i20]Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic:
Principal Neighbourhood Aggregation for Graph Nets. CoRR abs/2004.05718 (2020) - [i19]Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell:
Pointer Graph Networks. CoRR abs/2006.06380 (2020) - [i18]Stefan Spalevic, Petar Velickovic, Jovana J. Kovacevic, Mladen Nikolic:
Hierachial Protein Function Prediction with Tails-GNNs. CoRR abs/2007.12804 (2020) - [i17]Andreea Deac, Petar Velickovic, Ognjen Milinkovic, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic:
XLVIN: eXecuted Latent Value Iteration Nets. CoRR abs/2010.13146 (2020) - [i16]Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Velickovic, Théophane Weber:
On the role of planning in model-based deep reinforcement learning. CoRR abs/2011.04021 (2020) - [i15]Lovro Vrcek, Petar Velickovic, Mile Sikic:
A step towards neural genome assembly. CoRR abs/2011.05013 (2020)
2010 – 2019
- 2019
- [b1]Petar Velickovic:
The resurgence of structure in deep neural networks. University of Cambridge, UK, 2019 - [j4]Andreea Deac, Petar Velickovic, Pietro Sormanni:
Attentive Cross-Modal Paratope Prediction. J. Comput. Biol. 26(6): 536-545 (2019) - [c8]Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm:
Deep Graph Infomax. ICLR (Poster) 2019 - [i14]Alexander G. Rakowski, Petar Velickovic, Enrico Dall'Ara, Pietro Liò:
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data. CoRR abs/1901.03906 (2019) - [i13]Felix L. Opolka, Aaron Solomon, Catalina Cangea, Petar Velickovic, Pietro Liò, R. Devon Hjelm:
Spatio-Temporal Deep Graph Infomax. CoRR abs/1904.06316 (2019) - [i12]Andreea Deac, Yu-Hsiang Huang, Petar Velickovic, Pietro Liò, Jian Tang:
Drug-Drug Adverse Effect Prediction with Graph Co-Attention. CoRR abs/1905.00534 (2019) - [i11]Petar Velickovic, Rex Ying, Matilde Padovano, Raia Hadsell, Charles Blundell:
Neural Execution of Graph Algorithms. CoRR abs/1910.10593 (2019) - [i10]Carlos Purves, Catalina Cangea, Petar Velickovic:
The PlayStation Reinforcement Learning Environment (PSXLE). CoRR abs/1912.06101 (2019) - 2018
- [j3]Edgar Liberis, Petar Velickovic, Pietro Sormanni, Michele Vendruscolo, Pietro Liò:
Parapred: antibody paratope prediction using convolutional and recurrent neural networks. Bioinform. 34(17): 2944-2950 (2018) - [c7]Ioana Bica, Petar Velickovic, Hui Xiao:
Multi-omics data integration using cross-modal neural networks. ESANN 2018 - [c6]Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio:
Graph Attention Networks. ICLR (Poster) 2018 - [c5]Akhil Mathur, Tianlin Zhang, Sourav Bhattacharya, Petar Velickovic, Leonid Joffe, Nicholas D. Lane, Fahim Kawsar, Pietro Liò:
Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices. IPSN 2018: 200-211 - [c4]Laurynas Karazija, Petar Velickovic, Pietro Liò:
Automatic Inference of Cross-Modal Connection Topologies for X-CNNs. ISNN 2018: 54-63 - [c3]Petar Velickovic, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville:
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data. PervasiveHealth 2018: 178-186 - [i9]Laurynas Karazija, Petar Velickovic, Pietro Liò:
Automatic Inference of Cross-modal Connection Topologies for X-CNNs. CoRR abs/1805.00987 (2018) - [i8]Andreea Deac, Petar Velickovic, Pietro Sormanni:
Attentive cross-modal paratope prediction. CoRR abs/1806.04398 (2018) - [i7]Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm:
Deep Graph Infomax. CoRR abs/1809.10341 (2018) - [i6]Catalina Cangea, Petar Velickovic, Nikola Jovanovic, Thomas Kipf, Pietro Liò:
Towards Sparse Hierarchical Graph Classifiers. CoRR abs/1811.01287 (2018) - 2017
- [c2]Petar Velickovic, Nicholas D. Lane, Sourav Bhattacharya, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville:
Scaling health analytics to millions without compromising privacy using deep distributed behavior models. PervasiveHealth 2017: 92-100 - [i5]Catalina Cangea, Petar Velickovic, Pietro Liò:
XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification. CoRR abs/1709.00572 (2017) - [i4]Petar Velickovic, Laurynas Karazija, Nicholas D. Lane, Sourav Bhattacharya, Edgar Liberis, Pietro Liò, Angela Chieh, Otmane Bellahsen, Matthieu Vegreville:
Cross-modal Recurrent Models for Human Weight Objective Prediction from Multimodal Time-series Data. CoRR abs/1709.08073 (2017) - [i3]Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio:
Graph Attention Networks. CoRR abs/1710.10903 (2017) - [i2]Momchil Peychev, Petar Velickovic, Pietro Liò:
Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders. CoRR abs/1711.09159 (2017) - 2016
- [j2]Petar Velickovic, Pietro Liò:
Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types. Bioinform. 32(16): 2562-2564 (2016) - [j1]Petar Velickovic, Pietro Liò:
Molecular multiplex network inference using Gaussian mixture hidden Markov models. J. Complex Networks 4(4): 561-574 (2016) - [c1]Petar Velickovic, Duo Wang, Nicholas D. Lane, Pietro Liò:
X-CNN: Cross-modal convolutional neural networks for sparse datasets. SSCI 2016: 1-8 - [i1]Petar Velickovic, Duo Wang, Nicholas D. Lane, Pietro Liò:
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets. CoRR abs/1610.00163 (2016)
Coauthor Index
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