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Message Passing Neural Processes


Sep 29, 2020
Ben Day, Cătălina Cangea, Arian R. Jamasb, Pietro Liò

* 18 pages, 6 figures. The first two authors contributed equally 

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Generative Graph Perturbations for Scene Graph Prediction


Jul 11, 2020
Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky

* https://oolworkshop.github.io/program/ool_21.html, ICML Workshop 2020 on "Object-Oriented Learning (OOL): Perception, Representation, and Reasoning" 

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Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks


Jul 06, 2020
Péter Mernyei, Cătălina Cangea

* Graph Representation Learning and Beyond workshop (ICML 2020) 

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Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation


May 17, 2020
Boris Knyazev, Harm de Vries, Cătălina Cangea, Graham W. Taylor, Aaron Courville, Eugene Belilovsky

* 17 pages, the code is available at https://github.com/bknyaz/sgg 

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Deep Graph Mapper: Seeing Graphs through the Neural Lens


Feb 20, 2020
Cristian Bodnar, Cătălina Cangea, Pietro Liò

* 13 pages, 10 figures 

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The PlayStation Reinforcement Learning Environment (PSXLE)


Dec 12, 2019
Carlos Purves, Cătălina Cangea, Petar Veličković


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VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering


Aug 14, 2019
Cătălina Cangea, Eugene Belilovsky, Pietro Liò, Aaron Courville

* To appear at BMVC 2019. 15 pages, 5 figures 

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Spatio-Temporal Deep Graph Infomax


Apr 12, 2019
Felix L. Opolka, Aaron Solomon, Cătălina Cangea, Petar Veličković, Pietro Liò, R Devon Hjelm

* 6 pages, 2 figures, Representation Learning on Graphs and Manifolds Workshop of the International Conference on Learning Representations (ICLR) 

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Structure-Based Networks for Drug Validation


Nov 21, 2018
Cătălina Cangea, Arturas Grauslys, Pietro Liò, Francesco Falciani

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 

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Towards Sparse Hierarchical Graph Classifiers


Nov 03, 2018
Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, Pietro Liò

* To appear in the Workshop on Relational Representation Learning (R2L) at NIPS 2018. 6 pages, 3 figures 

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