X-CNN: Cross-modal convolutional neural networks for sparse datasets. - Petar Velickovic; Duo Wang; Nicholas D. Lane; Pietro Liò (SSCI, 2016)

Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types. - Petar Velickovic; Pietro Liò (Bioinform., 2016)

Molecular multiplex network inference using Gaussian mixture hidden Markov models. - Petar Velickovic; Pietro Liò (J. Complex Networks, 2016)

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 (PervasiveHealth, 2017)

XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification. - Catalina Cangea; 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)

Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders. - Momchil Peychev; Petar Velickovic; Pietro Liò (CoRR, 2017)

Multi-omics data integration using cross-modal neural networks. - Ioana Bica; Petar Velickovic; Hui Xiao (ESANN, 2018)

Graph Attention Networks. - Petar Velickovic; Guillem Cucurull; Arantxa Casanova; Adriana Romero; Pietro Liò; Yoshua Bengio (ICLR (Poster), 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 (IPSN, 2018)

Automatic Inference of Cross-Modal Connection Topologies for X-CNNs. - Laurynas Karazija; Petar Velickovic; Pietro Liò (ISNN, 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 (PervasiveHealth, 2018)

Parapred: antibody paratope prediction using convolutional and recurrent neural networks. - Edgar Liberis; Petar Velickovic; Pietro Sormanni; Michele Vendruscolo; Pietro Liò (Bioinform., 2018)

Towards Sparse Hierarchical Graph Classifiers. - Catalina Cangea; Petar Velickovic; Nikola Jovanovic 001; Thomas Kipf; Pietro Liò (CoRR, 2018)

Deep Graph Infomax. - Petar Velickovic; William Fedus; William L. Hamilton; Pietro Liò; Yoshua Bengio; R. Devon Hjelm (ICLR (Poster), 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)

Spatio-Temporal Deep Graph Infomax. - Felix L. Opolka; Aaron Solomon; Catalina Cangea; Petar Velickovic; Pietro Liò; R. Devon Hjelm (CoRR, 2019)

Drug-Drug Adverse Effect Prediction with Graph Co-Attention. - Andreea Deac; Yu-Hsiang Huang; Petar Velickovic; Pietro Liò; Jian Tang 005 (CoRR, 2019)

The PlayStation Reinforcement Learning Environment (PSXLE). - Carlos Purves; Catalina Cangea; Petar Velickovic (CoRR, 2019)

Attentive Cross-Modal Paratope Prediction. - Andreea Deac; Petar Velickovic; Pietro Sormanni (J. Comput. Biol., 2019)

The resurgence of structure in deep neural networks - Petar Velickovic (2019)

Neural Execution of Graph Algorithms. - Petar Velickovic; Rex Ying; Matilde Padovano; Raia Hadsell; Charles Blundell (ICLR, 2020)

Principal Neighbourhood Aggregation for Graph Nets. - Gabriele Corso; Luca Cavalleri; Dominique Beaini; Pietro Liò; Petar Velickovic (NeurIPS, 2020)

Pointer Graph Networks. - Petar Velickovic; Lars Buesing; Matthew C. Overlan; Razvan Pascanu; Oriol Vinyals; Charles Blundell (NeurIPS, 2020)

Hierachial Protein Function Prediction with Tails-GNNs. - Stefan Spalevic; Petar Velickovic; Jovana J. Kovacevic; Mladen Nikolic (CoRR, 2020)

XLVIN: eXecuted Latent Value Iteration Nets. - Andreea Deac; Petar Velickovic; Ognjen Milinkovic; Pierre-Luc Bacon; Jian Tang 005; Mladen Nikolic (CoRR, 2020)

A step towards neural genome assembly. - Lovro Vrcek; Petar Velickovic; Mile Sikic (CoRR, 2020)

XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification. - Catalina Cangea; Petar Velickovic; Pietro Liò (IEEE Trans. Neural Networks Learn. Syst., 2020)

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; et al. (CIKM, 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 001; Lars Holger Buesing; Petar Velickovic; Theophane Weber (ICLR, 2021)

Neural Distance Embeddings for Biological Sequences. - Gabriele Corso; Zhitao Ying; Michal Pándy; Petar Velickovic; Jure Leskovec; Pietro Liò (NeurIPS, 2021)

Neural Algorithmic Reasoners are Implicit Planners. - Andreea Deac; Petar Velickovic; Ognjen Milinkovic; Pierre-Luc Bacon; Jian Tang 005; Mladen Nikolic (NeurIPS, 2021)

How to transfer algorithmic reasoning knowledge to learn new algorithms? - Louis-Pascal A. C. Xhonneux; Andreea Deac; Petar Velickovic; Jian Tang 005 (NeurIPS, 2021)

Predicting Patient Outcomes with Graph Representation Learning. - Emma Rocheteau; Catherine Tong; Petar Velickovic; Nicholas D. Lane; Pietro Liò (CoRR, 2021)

Bootstrapped Representation Learning on Graphs. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Rémi Munos; Petar Velickovic; Michal Valko (CoRR, 2021)

Persistent Message Passing. - Heiko Strathmann; Mohammadamin Barekatain; Charles Blundell; Petar Velickovic (CoRR, 2021)

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. - Michael M. Bronstein; Joan Bruna; Taco Cohen; Petar Velickovic (CoRR, 2021)

Neural message passing for joint paratope-epitope prediction. - Alice Del Vecchio; Andreea Deac; Pietro Liò; 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)

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)

Relating Graph Neural Networks to Structural Causal Models. - Matej Zecevic; Devendra Singh Dhami; Petar Velickovic; Kristian Kersting (CoRR, 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; et al. (Nat., 2021)

Neural algorithmic reasoning. - Petar Velickovic; Charles Blundell (Patterns, 2021)

Algorithmic Concept-Based Explainable Reasoning. - Dobrik Georgiev; Pietro Barbiero; Dmitry Kazhdan; Petar Velickovic; Pietro Lió (AAAI, 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 (ICLR, 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 (ICLR, 2022)

The CLRS Algorithmic Reasoning Benchmark. - Petar Velickovic; Adrià Puigdomènech Badia; David Budden; Razvan Pascanu; Andrea Banino; Misha Dashevskiy; Raia Hadsell; Charles Blundell (ICML, 2022)

Expander Graph Propagation. - Andreea Deac; Marc Lackenby; Petar Velickovic (LoG, 2022)

Continuous Neural Algorithmic Planners. - Yu He; Petar Velickovic; Pietro Liò; Andreea Deac (LoG, 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; et al. (LoG, 2022)

Learnable Commutative Monoids for Graph Neural Networks. - Euan Ong; Petar Velickovic (LoG, 2022)

Learning Graph Search Heuristics. - Michal Pándy; Weikang Qiu; Gabriele Corso; Petar Velickovic; Zhitao Ying; Jure Leskovec; Pietro Liò (LoG, 2022)

Reasoning-Modulated Representations. - Petar Velickovic; Matko Bosnjak; Thomas Kipf; Alexander Lerchner; Raia Hadsell; Razvan Pascanu; Charles Blundell (LoG, 2022)

Utility of Equivariant Message Passing in Cortical Mesh Segmentation. - Dániel Unyi; Ferdinando Insalata; Petar Velickovic; Bálint Gyires-Tóth (MIUA, 2022)

Learning to Configure Computer Networks with Neural Algorithmic Reasoning. - Luca Beurer-Kellner; Martin T. Vechev; Laurent Vanbever; Petar Velickovic (NeurIPS, 2022)

Graph Neural Networks are Dynamic Programmers. - Andrew Joseph Dudzik; Petar Velickovic (NeurIPS, 2022)

Sheaf Neural Networks with Connection Laplacians. - Federico Barbero; Cristian Bodnar; Haitz Sáez de Ocáriz Borde; Michael M. Bronstein; Petar Velickovic; Pietro Liò (TAG-ML, 2022)

Message passing all the way up. - Petar Velickovic (CoRR, 2022)

Learning heuristics for A. - Danilo Numeroso; Davide Bacciu; Petar Velickovic (CoRR, 2022)

Dual Algorithmic Reasoning. - Danilo Numeroso; Davide Bacciu; Petar Velickovic (ICLR, 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 (ICML, 2023)

Neural Algorithmic Reasoning with Causal Regularisation. - Beatrice Bevilacqua; Kyriacos Nikiforou; Borja Ibarz; Ioana Bica; Michela Paganini; Charles Blundell; Jovana Mitrovic; Petar Velickovic (ICML, 2023)

Asynchronous Algorithmic Alignment With Cocycles. - Andrew Joseph Dudzik; Tamara von Glehn; Razvan Pascanu; Petar Velickovic (LoG, 2023)

Parallel Algorithms Align With Neural Execution. - Valerie Engelmayer; Dobrik Georgiev; Petar Velickovic (LoG, 2023)

Recursive Algorithmic Reasoning. - Jonas Jürß; Dulhan Hansaja Jayalath; Petar Velickovic (LoG, 2023)

Latent Space Representations of Neural Algorithmic Reasoners. - Vladimir V. Mirjanic; Razvan Pascanu; Petar Velickovic (LoG, 2023)

Affinity-Aware Graph Networks. - Ameya Velingker; Ali Kemal Sinop; Ira Ktena; Petar Velickovic; Sreenivas Gollapudi (NeurIPS, 2023)

Everything is Connected: Graph Neural Networks. - Petar Velickovic (CoRR, 2023)

Neural Priority Queues for Graph Neural Networks. - Rishabh Jain; Petar Velickovic; Pietro Liò (CoRR, 2023)

Geometric Epitope and Paratope Prediction. - Marco Pegoraro 002; Clémentine C. J. Dominé; Emanuele Rodolà; Petar Velickovic; Andreea Deac (CoRR, 2023)

TacticAI: an AI assistant for football tactics. - Zhe Wang 055; Petar Velickovic; Daniel Hennes; Nenad Tomasev; Laurel Prince; Michael Kaisers; Yoram Bachrach; Romuald Elie; Li Kevin Wenliang; Federico Piccinini; William Spearman; Ian Graham; et al. (CoRR, 2023)

Combinatorial Optimization and Reasoning with Graph Neural Networks. - Quentin Cappart; Didier Chételat; Elias B. Khalil; Andrea Lodi 001; Christopher Morris 001; Petar Velickovic (J. Mach. Learn. Res., 2023)

Scientific discovery in the age of artificial intelligence. - Hanchen Wang 002; Tianfan Fu; Yuanqi Du; Wenhao Gao 001; Kexin Huang; Ziming Liu; Payal Chandak; Shengchao Liu; Peter Van Katwyk; Andreea Deac; Anima Anandkumar; Karianne Bergen; et al. (Nat., 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., 2023)

Position: Categorical Deep Learning is an Algebraic Theory of All Architectures. - Bruno Gavranovic; Paul Lessard; Andrew Joseph Dudzik; Tamara von Glehn; João Guilherme Madeira Araújo; Petar Velickovic (ICML, 2024)

Position: Topological Deep Learning is the New Frontier for Relational Learning. - 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; et al. (ICML, 2024)

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; et al. (IJCAI, 2024)

Cayley Graph Propagation. - JJ Wilson; Maya Bechler-Speicher; Petar Velickovic (LoG, 2024)

Transformers need glasses! Information over-squashing in language tasks. - Federico Barbero; Andrea Banino; Steven Kapturowski; Dharshan Kumaran; João Guilherme Madeira Araújo; Oleksandr Vitvitskyi; Razvan Pascanu; Petar Velickovic (NeurIPS, 2024)

Position Paper: Challenges and Opportunities in Topological Deep Learning. - 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; et al. (CoRR, 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)

Temporal Graph Rewiring with Expander Graphs. - Katarina Petrovic; Shenyang Huang; Farimah Poursafaei; Petar Velickovic (CoRR, 2024)

The CLRS-Text Algorithmic Reasoning Language Benchmark. - Larisa Markeeva; Sean McLeish; Borja Ibarz; Wilfried Bounsi; Olga Kozlova; Alex Vitvitskyi; Charles Blundell; Tom Goldstein; Avi Schwarzschild; Petar Velickovic (CoRR, 2024)

Transformers meet Neural Algorithmic Reasoners. - Wilfried Bounsi; Borja Ibarz; Andrew Dudzik; Jessica B. Hamrick; Larisa Markeeva; Alex Vitvitskyi; Razvan Pascanu; Petar Velickovic (CoRR, 2024)

Commute-Time-Optimised Graphs for GNNs. - Igor Sterner; Shiye Su; Petar Velickovic (CoRR, 2024)

Recurrent Aggregators in Neural Algorithmic Reasoning. - Kaijia Xu; Petar Velickovic (CoRR, 2024)

softmax is not enough (for sharp out-of-distribution). - Petar Velickovic; Christos Perivolaropoulos; Federico Barbero; Razvan Pascanu (CoRR, 2024)

Positional Attention: Out-of-Distribution Generalization and Expressivity for Neural Algorithmic Reasoning. - Artur Back de Luca; George Giapitzakis; Shenghao Yang 002; Petar Velickovic; Kimon Fountoulakis (CoRR, 2024)

NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms. - Efimia Panagiotaki; Daniele De Martini; Lars Kunze; Petar Velickovic (CoRR, 2024)

Amplifying human performance in combinatorial competitive programming. - Petar Velickovic; Alex Vitvitskyi; Larisa Markeeva; Borja Ibarz; Lars Buesing; Matej Balog; Alexander Novikov 001 (CoRR, 2024)

Mastering Board Games by External and Internal Planning with Language Models. - John Schultz; Jakub Adámek; Matej Jusup; Marc Lanctot; Michael Kaisers; Sarah Perrin; Daniel Hennes; Jeremy Shar; Cannada Lewis; Anian Ruoss; Tom Zahavy; Petar Velickovic; et al. (CoRR, 2024)

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 (Trans. Mach. Learn. Res., 2024)

Round and Round We Go! What makes Rotary Positional Encodings useful? - Federico Barbero; Alex Vitvitskyi; Christos Perivolaropoulos; Razvan Pascanu; Petar Velickovic (ICLR, 2025)

What makes a good feedforward computational graph? - Alex Vitvitskyi; João G. M. Araújo; Marc Lackenby; Petar Velickovic (CoRR, 2025)

Why do LLMs attend to the first token? - Federico Barbero; Alvaro Arroyo; Xiangming Gu; Christos Perivolaropoulos; Michael M. Bronstein; Petar Velickovic; Razvan Pascanu (CoRR, 2025)

How Overconfidence in Initial Choices and Underconfidence Under Criticism Modulate Change of Mind in Large Language Models. - Dharshan Kumaran; Stephen M. Fleming; Larisa Markeeva; Joe Heyward; Andrea Banino; Mrinal Mathur; Razvan Pascanu; Simon Osindero; Benedetto De Martino; Petar Velickovic; Viorica Patraucean (CoRR, 2025)

Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists. - Owen Lewis; Neil Ghani; Andrew Dudzik; Christos Perivolaropoulos; Razvan Pascanu; Petar Velickovic (CoRR, 2025)

Optimizers Qualitatively Alter Solutions And We Should Leverage This. - Razvan Pascanu; Clare Lyle; Ionut-Vlad Modoranu; Naima Elosegui Borras; Dan Alistarh; Petar Velickovic; Sarath Chandar; Soham De; James Martens (CoRR, 2025)