| 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 | 2246 | 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 | 902 | 2023 |
| A clinically applicable approach to continuous prediction of future acute kidney injury Nature 572 (7767), 116-119, 2019 | 831 | 2019 |
| Gemini: A Family of Highly Capable Multimodal Models G Team https://arxiv.org/abs/2312.11805, 2023 | 407 | 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 | 390 | 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 | 254 | 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 | 228 | 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 | 219 | 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 | 129 | 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 | 123 | 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 | 78 | 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 | 78 | 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 | 70 | 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 | 69 | 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 | 68 | 2011 |
| Class imbalance and the curse of minority hubs N Tomašev, D Mladenić Knowledge-Based Systems 53, 157-172, 2013 | 62 | 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 | 61 | 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 | 59 | 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 | 59 | 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 | 51 | 2023 |
Dunja MladenicSenior researcher at J. Stefan InstituteVerified email at ijs.si
