Cornebise, Julien

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

Weight Uncertainty in Neural Networks

Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015) · arXiv

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine Learning

Cited by 1,342

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

Ai for social good: unlocking the opportunity for positive impact

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. V. D., Mugisha, F., et al. (2020) · Nature Communications

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 357

Opportunities and Risks of LLMs for Scalable Deliberation with Polis

Small, C. T., Vendrov, I., Durmus, E., Homaei, H., Barry, E., Cornebise, J., Suzman, T., Ganguli, D., & Megill, C. (2023) · arXiv

Ethics and Social Impacts of AIPolitical Theory and Democracy

Cited by 20

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

DECoVaC: Design of Experiments with Controlled Variability Components

Boquet, T., Delisle, L., Kochetkov, D., Schucher, N., Atighehchian, P., Oreshkin, B., & Cornebise, J. (2019) · arXiv

Machine Learning and Data ClassificationOptimal Experimental Design Methods

Cited by 1