| Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... Nature Machine Intelligence 3 (3), 199-217, 2021 | 1009 | 2021 |
| Optimal mass transport: Signal processing and machine-learning applications S Kolouri, SR Park, M Thorpe, D Slepcev, GK Rohde IEEE signal processing magazine 34 (4), 43-59, 2017 | 545 | 2017 |
| Analysis of -Laplacian Regularization in Semisupervised Learning D Slepcev, M Thorpe SIAM Journal on Mathematical Analysis 51 (3), 2085-2120, 2019 | 153 | 2019 |
| Poisson learning: Graph based semi-supervised learning at very low label rates J Calder, B Cook, M Thorpe, D Slepcev International Conference on Machine Learning, 1306-1316, 2020 | 112 | 2020 |
| GRAND++: Graph neural diffusion with a source term M Thorpe, T Nguyen, H Xia, T Strohmer, A Bertozzi, S Osher, B Wang ICLR, 2022 | 93 | 2022 |
| SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination F Liew, S Talwar, A Cross, BJ Willett, S Scott, N Logan, MK Siggins, ... EBioMedicine 87, 2023 | 92 | 2023 |
| A Transportation Distance for Signal Analysis M Thorpe, S Park, S Kolouri, GK Rohde, D Slepčev Journal of mathematical imaging and vision 59, 187-210, 2017 | 85 | 2017 |
| Large data and zero noise limits of graph-based semi-supervised learning algorithms MM Dunlop, D Slepčev, AM Stuart, M Thorpe Applied and Computational Harmonic Analysis 49 (2), 655-697, 2020 | 65 | 2020 |
| Deep limits of residual neural networks M Thorpe, Y van Gennip arXiv preprint arXiv:1810.11741, 2018 | 64 | 2018 |
| Introduction to optimal transport M Thorpe Notes of Course at University of Cambridge, 2018 | 53 | 2018 |
| Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease F Liew, C Efstathiou, S Fontanella, M Richardson, R Saunders, ... Nature immunology 25 (4), 607-621, 2024 | 47 | 2024 |
| Rates of convergence for Laplacian semi-supervised learning with low labeling rates J Calder, D Slepčev, M Thorpe Research in the Mathematical Sciences 10 (1), 10, 2023 | 43 | 2023 |
| The impact of imputation quality on machine learning classifiers for datasets with missing values T Shadbahr, M Roberts, J Stanczuk, J Gilbey, P Teare, S Dittmer, ... Communications Medicine 3 (1), 139, 2023 | 37 | 2023 |
| Transport-based analysis, modeling, and learning from signal and data distributions S Kolouri, S Park, M Thorpe, D Slepčev, GK Rohde arXiv preprint arXiv:1609.04767, 2016 | 36 | 2016 |
| Sliced optimal partial transport Y Bai, B Schmitzer, M Thorpe, S Kolouri Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 34 | 2023 |
| The Linearized Hellinger--Kantorovich Distance T Cai, J Cheng, B Schmitzer, M Thorpe SIAM Journal on Imaging Sciences 15 (1), 45-83, 2022 | 28 | 2022 |
| Convergence of the -Means Minimization Problem using -Convergence M Thorpe, F Theil, AM Johansen, N Cade SIAM Journal on Applied Mathematics 75 (6), 2444-2474, 2015 | 27 | 2015 |
| Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... arXiv preprint arXiv:2008.06388, 2020 | 26 | 2020 |
| Deep limits of residual neural networks M Thorpe, Y van Gennip Research in the Mathematical Sciences 10 (1), 6, 2023 | 22 | 2023 |
| AIX-COVNET M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... Common pitfalls and recommendations for using machine learning to detect and …, 2021 | 21 | 2021 |
