activity
20182021
most citedClique pooling for graph classification

31 citations · 48 across the 4 of their papers we have counts for

collaborators

8 papers

q-bio.QM2021

Structure-aware generation of drug-like molecules

Pavol Drotár, Arian Rokkum Jamasb, Ben Day +2

Structure-based drug design involves finding ligand molecules that exhibit structural and chemical complementarity to protein pockets. Deep generative methods have shown promise in…

cs.LG20206 cited

Message Passing Neural Processes

Ben Day, Cătălina Cangea, Arian R. Jamasb +1

Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity. However,…

cs.LG2020

Uncertainty in Neural Relational Inference Trajectory Reconstruction

Vasileios Karavias, Ben Day, Pietro Liò

Neural networks used for multi-interaction trajectory reconstruction lack the ability to estimate the uncertainty in their outputs, which would be useful to better analyse and unde…

cs.CV2019

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

Cătălina Cangea, Eugene Belilovsky, Pietro Liò +1

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given…

cs.LG201911 cited

On Graph Classification Networks, Datasets and Baselines

Enxhell Luzhnica, Ben Day, Pietro Liò

Graph classification receives a great deal of attention from the non-Euclidean machine learning community. Recent advances in graph coarsening have enabled the training of deeper n…

cs.LG201931 cited

Clique pooling for graph classification

Enxhell Luzhnica, Ben Day, Pietro Lio'

We propose a novel graph pooling operation using cliques as the unit pool. As this approach is purely topological, rather than featural, it is more readily interpretable, a better…