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Julien Cornebise

Showing results (1-10 of 7) with videos related to

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Statistics in Medicine|September 11, 2012
Adaptive Markov chain Monte Carlo forward projection for statistical analysis in epidemic modelling of human papillomavirusIgor A Korostil, Gareth W Peters, Julien Cornebise, et al.
Statistical Applications in Genetics and Molecular Biology|March 19, 2013
On optimality of kernels for approximate Bayesian computation using sequential Monte CarloSarah Filippi, Chris P Barnes, Julien Cornebise, et al.
F1000Research|July 8, 2017
Automated analysis of retinal imaging using machine learning techniques for computer visionJeffrey De Fauw, Pearse Keane, Nenad Tomasev, et al.
Nature|August 2, 2019
A clinically applicable approach to continuous prediction of future acute kidney injuryNenad Tomašev, Xavier Glorot, Jack W Rae, et al.
Nature Communications|May 20, 2020
AI for social good: unlocking the opportunity for positive impactNenad Tomašev, Julien Cornebise, Frank Hutter, et al.
Nature Protocols|May 6, 2021
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health recordsNenad Tomašev, Natalie Harris, Sebastien Baur, et al.
Nature Medicine|August 15, 2018
Clinically applicable deep learning for diagnosis and referral in retinal diseaseJeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Statistics in Medicine|September 11, 2012
Adaptive Markov chain Monte Carlo forward projection for statistical analysis in epidemic modelling of human papillomavirusIgor A Korostil, Gareth W Peters, Julien Cornebise, et al.
Statistical Applications in Genetics and Molecular Biology|March 19, 2013
On optimality of kernels for approximate Bayesian computation using sequential Monte CarloSarah Filippi, Chris P Barnes, Julien Cornebise, et al.
F1000Research|July 8, 2017
Automated analysis of retinal imaging using machine learning techniques for computer visionJeffrey De Fauw, Pearse Keane, Nenad Tomasev, et al.
Nature|August 2, 2019
A clinically applicable approach to continuous prediction of future acute kidney injuryNenad Tomašev, Xavier Glorot, Jack W Rae, et al.
Nature Communications|May 20, 2020
AI for social good: unlocking the opportunity for positive impactNenad Tomašev, Julien Cornebise, Frank Hutter, et al.
Nature Protocols|May 6, 2021
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health recordsNenad Tomašev, Natalie Harris, Sebastien Baur, et al.
Nature Medicine|August 15, 2018
Clinically applicable deep learning for diagnosis and referral in retinal diseaseJeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, et al.
Pageof 1