| Towards Sparse Hierarchical Graph Classifiers C Cangea, P Veličković, N Jovanović, T Kipf, P Liò Relational Representation Learning Workshop (NeurIPS), 2018 | 287* | 2018 |
| Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks P Mernyei, C Cangea Graph Representation Learning and Beyond Workshop (ICML), 2020 | 211 | 2020 |
| General-purpose, long-context autoregressive modeling with Perceiver AR C Hawthorne, A Jaegle, C Cangea, S Borgeaud, C Nash, M Malinowski, ... The Thirty-ninth International Conference on Machine Learning (ICML), 2022 | 61 | 2022 |
| Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation B Knyazev, H de Vries, C Cangea, GW Taylor, A Courville, E Belilovsky British Machine Vision Conference (BMVC), 2020 | 60* | 2020 |
| Spatio-Temporal Deep Graph Infomax FL Opolka, A Solomon, C Cangea, P Veličković, P Liò, RD Hjelm Representation Learning on Graphs and Manifolds Workshop (ICLR), 2019 | 40* | 2019 |
| Deep Graph Mapper: Seeing Graphs through the Neural Lens C Bodnar, C Cangea, P Lio Frontiers in Big Data - Machine Learning and Artificial Intelligence, 2021 | 36* | 2021 |
| XFlow: Cross-Modal Deep Neural Networks for Audiovisual Classification C Cangea, P Veličković, P Liò IEEE Transactions on Neural Networks and Learning Systems, 2019 | 32* | 2019 |
| Generative compositional augmentations for scene graph prediction, IEEE B Knyazev, H de Vries, C Cangea, GW Taylor, A Courville, E Belilovsky CVF International Conference on Computer Vision (ICCV), 15827-15837, 2021 | 28* | 2021 |
| Structure-aware generation of drug-like molecules P Drotár, AR Jamasb, B Day, C Cangea, P Liò Machine Learning in Structural Biology Workshop (NeurIPS), 2021 | 25 | 2021 |
| Deep learning for protein–protein interaction site prediction AR Jamasb, B Day, C Cangea, P Liò, TL Blundell Proteomics data analysis, 263-288, 2021 | 21* | 2021 |
| VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering C Cangea, E Belilovsky, P Liò, A Courville British Machine Vision Conference (BMVC), 2019 | 12 | 2019 |
| Message Passing Neural Processes C Cangea, B Day, AR Jamasb, P Lio Geometrical and Topological Representation Learning Workshop (ICLR), 2022 | 10* | 2022 |
| The PlayStation Reinforcement Learning Environment (PSXLE) C Purves, C Cangea, P Veličković Deep Reinforcement Learning Workshop (NeurIPS), 2019 | 5 | 2019 |
| Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task J Kossen, C Cangea, E Vértes, A Jaegle, V Patraucean, I Ktena, ... Transactions on Machine Learning Research (TMLR), 2023 | 4 | 2023 |
| Structure-Based Networks for Drug Validation C Cangea, A Grauslys, P Liò, F Falciani Machine Learning for Health (ML4H) Workshop (NeurIPS), 2018 | 1 | 2018 |
| Selective acquisition for multi-modal temporal data JL KOSSEN, DCM BELGRAVE, N TOMASEV, CC CANGEA, SI KTENA, ... WO Patent 2,024,084,097, 2024 | | 2024 |
| Generating sequences of data elements using cross-attention operations CGM Hawthorne, AC Jaegle, CC Cangea, SBD Avocat, CTC Nash, ... US Patent 20,230,244,907, 2023 | | 2023 |
| Code change graph node matching with machine learning C Cangea, Q Zhang US Patent 20,220,019,410, 2022 | | 2022 |
| Exploiting multimodality and structure in world representations C Cangea University of Cambridge, 2021 | | 2021 |
| Proceedings of the Fourth Workshop on Visually Grounded Interaction and Language C Cangea, A Das, D Hudson, J Krantz, S Lee, J Mao, F Strub, A Suhr, ... Proceedings of the Fourth Workshop on Visually Grounded Interaction and Language, 2021 | | 2021 |
