| Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients E Jimenez-Solem, TS Petersen, C Hansen, C Hansen, C Lioma, C Igel, ... Scientific reports 11 (1), 3246, 2021 | 81 | 2021 |
| CMA-ES with optimal covariance update and storage complexity O Krause, DR Arbonès, C Igel Advances in neural information processing systems 29, 2016 | 44 | 2016 |
| Is segmentation uncertainty useful? S Czolbe, K Arnavaz, O Krause, A Feragen Information Processing in Medical Imaging: 27th International Conference …, 2021 | 36 | 2021 |
| Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning S Shakibfar, O Krause, C Lund-Andersen, A Aranda, J Moll, TO Andersen, ... Ep Europace 21 (2), 268-274, 2019 | 33 | 2019 |
| A more efficient rank-one covariance matrix update for evolution strategies O Krause, C Igel Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms …, 2015 | 30 | 2015 |
| A loss function for generative neural networks based on watson’s perceptual model S Czolbe, O Krause, I Cox, C Igel Advances in Neural Information Processing Systems 33, 2051-2061, 2020 | 27 | 2020 |
| Convolutional neural networks for segmentation and object detection of human semen MS Nissen, O Krause, K Almstrup, S Kjærulff, TT Nielsen, M Nielsen Image Analysis: 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway …, 2017 | 23 | 2017 |
| Unbounded population MO-CMA-ES for the bi-objective BBOB test suite O Krause, T Glasmachers, N Hansen, C Igel Proceedings of the 2016 on Genetic and Evolutionary Computation Conference …, 2016 | 22 | 2016 |
| Algorithms for estimating the partition function of restricted Boltzmann machines O Krause, A Fischer, C Igel Artificial Intelligence 278, 103195, 2020 | 19 | 2020 |
| A CMA-ES with multiplicative covariance matrix updates O Krause, T Glasmachers Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 18 | 2015 |
| Semantic similarity metrics for learned image registration S Czolbe, O Krause, A Feragen Medical Imaging with Deep Learning, 105-118, 2021 | 17 | 2021 |
| Approximation properties of DBNs with binary hidden units and real-valued visible units O Krause, A Fischer, T Glasmachers, C Igel International conference on machine learning, 419-426, 2013 | 17 | 2013 |
| Population-Contrastive-Divergence: Does consistency help with RBM training? O Krause, A Fischer, C Igel Pattern Recognition Letters 102, 1-7, 2018 | 16 | 2018 |
| Multimodal variational autoencoders for semi-supervised learning: In defense of product-of-experts S Kutuzova, O Krause, D McCloskey, M Nielsen, C Igel arXiv preprint arXiv:2101.07240, 2021 | 13 | 2021 |
| Autonomous estimation of high-dimensional coulomb diamonds from sparse measurements A Chatterjee, F Ansaloni, T Rasmussen, B Brovang, F Fedele, ... Physical Review Applied 18 (6), 064040, 2022 | 12 | 2022 |
| Sparse incomplete lu-decomposition for wave farm designs under realistic conditions DR Arbonès, NY Sergiienko, B Ding, O Krause, C Igel, M Wagner Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018 | 11 | 2018 |
| The Hessian estimation evolution strategy T Glasmachers, O Krause International Conference on Parallel Problem Solving from Nature, 597-609, 2020 | 10 | 2020 |
| Qualitative and quantitative assessment of step size adaptation rules O Krause, T Glasmachers, C Igel Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic …, 2017 | 9 | 2017 |
| DeepSim: Semantic similarity metrics for learned image registration S Czolbe, O Krause, A Feragen arXiv preprint arXiv:2011.05735, 2020 | 7 | 2020 |
| Learning Coulomb diamonds in large quantum dot arrays O Krause, A Chatterjee, F Kuemmeth, E van Nieuwenburg SciPost Physics 13 (4), 084, 2022 | 6 | 2022 |
Christian IgelDepartment of Computer Science, University of CopenhagenVerified email at diku.dk
