Nenad Tomasev
Cited by
Cited by
Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease
J Defauw, J Ledsam, Romera-Paredes B., N S., T Nenad, B S., A H., ...
Nature Medicine, 2018
Large language models encode clinical knowledge
K Singhal, S Azizi, T Tu, SS Mahdavi, J Wei, HW Chung, N Scales, ...
Nature 620 (7972), 172-180, 2023
A clinically applicable approach to continuous prediction of future acute kidney injury
Nature 572 (7767), 116-119, 2019
Gemini: A Family of Highly Capable Multimodal Models
G Team, 2023
Advancing mathematics by guiding human intuition with AI
A Davies, P Veličković, L Buesing, S Blackwell, D Zheng, N Tomašev, ...
Nature 600 (7887), 70-74, 2021
Towards expert-level medical question answering with large language models
K Singhal, T Tu, J Gottweis, R Sayres, E Wulczyn, L Hou, K Clark, S Pfohl, ...
arXiv preprint arXiv:2305.09617, 2023
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
The role of hubness in clustering high-dimensional data
N Tomasev, M Radovanovic, D Mladenic, M Ivanovic
IEEE transactions on knowledge and data engineering 26 (3), 739-751, 2013
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol
V Sounderajah, H Ashrafian, RM Golub, S Shetty, J De Fauw, L Hooft, ...
BMJ open 11 (6), e047709, 2021
Acquisition of chess knowledge in alphazero
T McGrath, A Kapishnikov, N Tomašev, A Pearce, M Wattenberg, ...
Proceedings of the National Academy of Sciences 119 (47), e2206625119, 2022
Fairness for unobserved characteristics: Insights from technological impacts on queer communities
N Tomasev, KR McKee, J Kay, S Mohamed
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 254-265, 2021
A probabilistic approach to nearest-neighbor classification: Naive hubness bayesian knn
N Tomasev, M Radovanović, D Mladenić, M Ivanović
Proceedings of the 20th ACM international conference on Information and …, 2011
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
S Azizi, L Culp, J Freyberg, B Mustafa, S Baur, S Kornblith, T Chen, ...
Nature Biomedical Engineering 7 (6), 756-779, 2023
Pushing the limits of self-supervised resnets: Can we outperform supervised learning without labels on imagenet?
N Tomasev, I Bica, B McWilliams, L Buesing, R Pascanu, C Blundell, ...
arXiv preprint arXiv:2201.05119, 2022
Nearest neighbor voting in high-dimensional data: Learning from past occurrences
N Tomasev, D Mladenic
2011 IEEE 11th International Conference on Data Mining Workshops, 1215-1218, 2011
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
Class imbalance and the curse of minority hubs
N Tomašev, D Mladenić
Knowledge-Based Systems 53, 157-172, 2013
Automated analysis of retinal imaging using machine learning techniques for computer vision
JC Jeffrey De Fauw, Pearse Keane1, Nenad Tomasev, Daniel Visentin, George ...
F1000Research, 2016
Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification
N Tomašev, M Radovanović, D Mladenić, M Ivanović
International Journal of Machine Learning and Cybernetics 5, 445-458, 2014
Hubness-aware shared neighbor distances for high-dimensional-nearest neighbor classification
N Tomašev, D Mladenić
Knowledge and information systems 39 (1), 89-122, 2014
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