activity
20162020
most citedOffline bilingual word vectors, orthogonal transformations and the inverted softmax

264 citations · 429 across the 7 of their papers we have counts for

collaborators

15 papers

cs.CL2020

Biomedical Concept Relatedness -- A large EHR-based benchmark

Claudia Schulz, Josh Levy-Kramer, Camille Van Assel +2

A promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a cons…

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.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.LG2019

Explaining Deep Learning Models with Constrained Adversarial Examples

Jonathan Moore, Nils Hammerla, Chris Watkins

Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decisio…

cs.CL20197 cited

Correlation Coefficients and Semantic Textual Similarity

Vitalii Zhelezniak, Aleksandar Savkov, April Shen +1

A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals…