| Weight uncertainty in neural network C Blundell, J Cornebise, K Kavukcuoglu, D Wierstra International conference on machine learning, 1613-1622, 2015 | 4544 | 2015 |
| Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ... Nature medicine 24 (9), 1342-1350, 2018 | 2576 | 2018 |
| A clinically applicable approach to continuous prediction of future acute kidney injury N Tomašev, X Glorot, JW Rae, M Zielinski, H Askham, A Saraiva, ... Nature 572 (7767), 116-119, 2019 | 974 | 2019 |
| AI for social good: unlocking the opportunity for positive impact N Tomašev, J Cornebise, F Hutter, S Mohamed, A Picciariello, B Connelly, ... Nature Communications 11 (1), 2468, 2020 | 308 | 2020 |
| On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo S Filippi, C Barnes, J Cornebise, MPH Stumpf | 162 | 2011 |
| Highres-net: Recursive fusion for multi-frame super-resolution of satellite imagery M Deudon, A Kalaitzis, I Goytom, MR Arefin, Z Lin, K Sankaran, ... arXiv preprint arXiv:2002.06460, 2020 | 142 | 2020 |
| Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with … G Carneiro, D Mateus, L Peter, A Bradley, JMRS Tavares, V Belagiannis, ... Springer, 2016 | 128* | 2016 |
| Adaptive methods for sequential importance sampling with application to state space models J Cornebise, É Moulines, J Olsson Statistics and Computing 18, 461-480, 2008 | 103 | 2008 |
| Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records N Tomašev, N Harris, S Baur, A Mottram, X Glorot, JW Rae, M Zielinski, ... Nature Protocols 16 (6), 2765-2787, 2021 | 93 | 2021 |
| Open high-resolution satellite imagery: The worldstrat dataset–with application to super-resolution J Cornebise, I Oršolić, F Kalaitzis Advances in Neural Information Processing Systems 35, 25979-25991, 2022 | 63 | 2022 |
| Automated analysis of retinal imaging using machine learning techniques for computer vision J De Fauw, P Keane, N Tomasev, D Visentin, G van den Driessche, ... F1000Research 5, 1573, 2017 | 63 | 2017 |
| A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter L Delisle, A Kalaitzis, K Majewski, A de Berker, M Marin, J Cornebise arXiv preprint arXiv:1902.03093, 2019 | 38 | 2019 |
| Adaptive Markov chain Monte Carlo forward projection for statistical analysis in epidemic modelling of human papillomavirus IA Korostil, GW Peters, J Cornebise, DG Regan Statistics in medicine 32 (11), 1917-1953, 2013 | 32 | 2013 |
| Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans C Chu, J De Fauw, N Tomasev, BR Paredes, C Hughes, J Ledsam, ... F1000Research 5, 2104, 2016 | 30 | 2016 |
| Opportunities and risks of LLMs for scalable deliberation with Polis CT Small, I Vendrov, E Durmus, H Homaei, E Barry, J Cornebise, ... arXiv preprint arXiv:2306.11932, 2023 | 29 | 2023 |
| Adaptive sequential Monte Carlo by means of mixture of experts J Cornebise, E Moulines, J Olsson Statistics and Computing 24, 317-337, 2014 | 17 | 2014 |
| Witnessing atrocities: quantifying villages destruction in Darfur with crowdsourcing and transfer learning J Cornebise, D Worrall, M Farfour, M Marin Proc. AI for Social Good NeurIPS2018 Workshop, NeurIPS’18, 2018 | 14 | 2018 |
| Signal processing systems JRM Cornebise, DJ Rezende, DP Wierstra US Patent 9,342,781, 2016 | 14 | 2016 |
| Adaptive Sequential Monte Carlo Methods J Cornebise University Pierre of Marie Curie, Paris, 45-76, 2009 | 14 | 2009 |
| Generalizable medical image analysis using segmentation and classification neural networks J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ... US Patent 10,878,601, 2020 | 10 | 2020 |
Charles BlundellResearch Scientist at DeepMindVerified email at google.com
