192 citations · 406 across the 11 of their papers we have counts for
13 papers
Mathematical Invariant-Enabled Topological Neural Networks for Molecular and Materials Property Prediction
Yiming Ren, Xiang Liu, Mustafa Hajij +2
Existing molecular and materials learning approaches often rely on a limited set of structural representations, which may capture only selected aspects of complex three-dimensional…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Andreea Deac, Yu-Hsiang Huang, Petar Veličković +2
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is u…
Spatio-Temporal Deep Graph Infomax
Felix L. Opolka, Aaron Solomon, Cătălina Cangea +3
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes ove…
ChronoMID - Cross-Modal Neural Networks for 3-D Temporal Medical Imaging Data
Alexander G. Rakowski, Petar Veličković, Enrico Dall'Ara +1
ChronoMID builds on the success of cross-modal convolutional neural networks (X-CNNs), making the novel application of the technique to medical imaging data. Specifically, this pap…
Towards Sparse Hierarchical Graph Classifiers
Cătălina Cangea, Petar Veličković, Nikola Jovanović +2
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. Wh…