J
Julien Cornebise
Researcher at University College London
Publications - 38
Citations - 5831
Julien Cornebise is an academic researcher from University College London. The author has contributed to research in topics: Particle filter & Monte Carlo method. The author has an hindex of 15, co-authored 34 publications receiving 4089 citations. Previous affiliations of Julien Cornebise include Google & École Normale Supérieure.
Papers
More filters
Journal ArticleDOI
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw,Joseph R. Ledsam,Bernardino Romera-Paredes,Stanislav Nikolov,Nenad Tomasev,Sam Blackwell,Harry Askham,Xavier Glorot,Brendan O'Donoghue,Daniel Visentin,George van den Driessche,Balaji Lakshminarayanan,Clemens Meyer,Faith Mackinder,Simon Bouton,Kareem Ayoub,Reena Chopra,Dominic King,Alan Karthikesalingam,Cian Hughes,Rosalind Raine,Julian Hughes,Dawn A Sim,Catherine A Egan,Adnan Tufail,Hugh Montgomery,Demis Hassabis,Geraint Rees,Trevor Back,Peng T. Khaw,Mustafa Suleyman,Julien Cornebise,Pearse A. Keane,Olaf Ronneberger +33 more
TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.
Posted Content
Weight Uncertainty in Neural Networks
TL;DR: This work introduces a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop, and shows how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems.
Proceedings Article
Weight Uncertainty in Neural Network
TL;DR: This work introduces a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop, and shows how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems.
Journal ArticleDOI
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomasev,Xavier Glorot,Jack W. Rae,Michal Zielinski,Harry Askham,Andre Saraiva,Anne Mottram,Clemens Meyer,Suman V. Ravuri,Ivan Protsyuk,Alistair Connell,Cian Hughes,Alan Karthikesalingam,Julien Cornebise,Hugh Montgomery,Geraint Rees,Chris Laing,Clifton R. Baker,Kelly S. Peterson,Ruth M. Reeves,Demis Hassabis,Dominic King,Mustafa Suleyman,Trevor Back,Christopher Nielson,Christopher Nielson,Joseph R. Ledsam,Shakir Mohamed +27 more
TL;DR: A deep learning approach that predicts the risk of acute kidney injury and provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests are developed.
Journal ArticleDOI
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
TL;DR: This paper discusses how to construct the perturbation kernels that are required in ABC SMC approaches, in order to construct a sequence of distributions that start out from a suitably defined prior and converge towards the unknown posterior.