Nenad Tomasev
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Citado por
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
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
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
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
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
The role of hubness in clustering high-dimensional data
N Tomašev, M Radovanović, D Mladenić, M Ivanović
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 183-195, 2011
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
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
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
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
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