309 citations · 1.2k across the 38 of their papers we have counts for
10 papers · 1 filter
Neural Execution of Graph Algorithms
Petar Veličković, Rex Ying, Matilde Padovano +2
Graph Neural Networks (GNNs) are a powerful representational tool for solving problems on graph-structured inputs. In almost all cases so far, however, they have been applied to di…
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…
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…
Deep Graph Infomax
Petar Veličković, William Fedus, William L. Hamilton +3
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual in…
Attentive cross-modal paratope prediction
Andreea Deac, Petar Veličković, Pietro Sormanni
Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able t…
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…
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova +3
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the short…
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
Petar Veličković, Laurynas Karazija, Nicholas D. Lane +6
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objecti…
XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification
Cătălina Cangea, Petar Veličković, Pietro Liò
In recent years, there have been numerous developments towards solving multimodal tasks, aiming to learn a stronger representation than through a single modality. Certain aspects o…
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets
Petar Veličković, Duo Wang, Nicholas D. Lane +1
In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as…