69 citations · 85 across the 3 of their papers we have counts for
5 papers
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…
Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders
Momchil Peychev, Petar Veličković, Pietro Liò
The notion of disentangled autoencoders was proposed as an extension to the variational autoencoder by introducing a disentanglement parameter , controlling the learning pressur…