activity
20182020
most citedRelational Graph Attention Networks

128 citations · 132 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG2020

Neural Temporal Point Processes For Modelling Electronic Health Records

Joseph Enguehard, Dan Busbridge, Adam Bozson +2

The modelling of Electronic Health Records (EHRs) has the potential to drive more efficient allocation of healthcare resources, enabling early intervention strategies and advancing…

cs.AI2020

Learning medical triage from clinicians using Deep Q-Learning

Albert Buchard, Baptiste Bouvier, Giulia Prando +10

Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately…

cs.CL2019

Correlations between Word Vector Sets

Vitalii Zhelezniak, April Shen, Daniel Busbridge +2

Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic text…

cs.CL20194 cited

Neural Language Priors

Joseph Enguehard, Dan Busbridge, Vitalii Zhelezniak +1

The choice of sentence encoder architecture reflects assumptions about how a sentence's meaning is composed from its constituent words. We examine the contribution of these archite…

cs.LG2019128 cited

Relational Graph Attention Networks

Dan Busbridge, Dane Sherburn, Pietro Cavallo +1

We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these…

cs.AI2018

Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks

Vitalii Zhelezniak, Dan Busbridge, April Shen +2

Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this beh…