Glorot, Xavier

Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 2,639

A clinically applicable approach to continuous prediction of future acute kidney injury

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Nature

Machine Learning in HealthcareAcute Kidney Injury Research

Cited by 1,086

A semantic matching energy function for learning with multi-relational data: Application to word-sense disambiguation

Bordes, A., Glorot, X., Weston, J., & Bengio, Y. (2014) · Machine Learning

Advanced Graph Neural NetworksTopic Modeling

Cited by 675

Training a First-Order Theorem Prover from Synthetic Data

Firoiu, V., Aygun, E., Anand, A., Ahmed, Z., Glorot, X., Orseau, L., Zhang, L., Precup, D., & Mourad, S. (2021) · arXiv

Logic, programming, and type systemsLogic, Reasoning, and Knowledge

Cited by 15

Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Research Square

Machine Learning in HealthcareAcute Kidney Injury Research

Cited by 3