| MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets PA Mattei, J Frellsen Proceedings of the 36th International Conference on Machine Learning, PMLR …, 2019 | 450 | 2019 |
| A probabilistic model of RNA conformational space J Frellsen, I Moltke, M Thiim, KV Mardia, J Ferkinghoff-Borg, T Hamelryck PLoS computational biology 5 (6), e1000406, 2009 | 141 | 2009 |
| Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation D Ulmer, C Hardmeier, J Frellsen Transactions on Machine Learning Research, 2023 | 138* | 2023 |
| Spherical convolutions and their application in molecular modelling. W Boomsma, J Frellsen Advances in Neural Information Processing Systems 30 (NeurIPS 2017) 2, 6, 2017 | 119 | 2017 |
| not-MIWAE: Deep Generative Modelling with Missing not at Random Data NB Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2021 | 111 | 2021 |
| Potentials of mean force for protein structure prediction vindicated, formalized and generalized T Hamelryck, M Borg, M Paluszewski, J Paulsen, J Frellsen, C Andreetta, ... PloS one 5 (11), e13714, 2010 | 109 | 2010 |
| Hierarchical VAEs Know What They Don't Know JD Havtorn, J Frellsen, S Hauberg, L Maaløe Proceedings of the 38th International Conference on Machine Learning (ICML …, 2021 | 98 | 2021 |
| Beyond rotamers: a generative, probabilistic model of side chains in proteins T Harder, W Boomsma, M Paluszewski, J Frellsen, KE Johansson, ... BMC bioinformatics 11 (1), 306, 2010 | 84 | 2010 |
| Leveraging the exact likelihood of deep latent variable models PA Mattei, J Frellsen Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018 | 82 | 2018 |
| How to deal with missing data in supervised deep learning? NB Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2022 | 67 | 2022 |
| Inference of structure ensembles of flexible biomolecules from sparse, averaged data S Olsson, J Frellsen, W Boomsma, KV Mardia, T Hamelryck PloS one 8 (11), e79439, 2013 | 63 | 2013 |
| deep-significance: Easy and meaningful signifcance testing in the age of neural networks D Ulmer, C Hardmeier, J Frellsen ML Evaluation Standards Workshop at the Tenth International Conference on …, 2022 | 57* | 2022 |
| Euclidean neural networks: e3nn M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... Version 0.5. 0, 2022 | 52* | 2022 |
| Euclidean neural networks: e3nn M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... Version 0.5. 0, 2022 | 51 | 2022 |
| Adaptable probabilistic mapping of short reads using position specific scoring matrices P Kerpedjiev, J Frellsen, S Lindgreen, A Krogh BMC bioinformatics 15 (1), 100, 2014 | 51 | 2014 |
| PHAISTOS: a framework for Markov chain Monte Carlo simulation and inference of protein structure W Boomsma, J Frellsen, T Harder, S Bottaro, KE Johansson, P Tian, ... Journal of computational chemistry 34 (19), 1697-1705, 2013 | 49 | 2013 |
| Asap: a framework for over-representation statistics for transcription factor binding sites TT Marstrand, J Frellsen, I Moltke, M Thiim, E Valen, D Retelska, A Krogh PLoS One 3 (2), e1623, 2008 | 48 | 2008 |
| Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation S Wiqvist, M Pierre-Alexandre, U Picchini, J Frellsen Proceedings of the 36th International Conference on Machine Learning, PMLR …, 2019 | 46 | 2019 |
| Deep transfer learning can be used for the detection of hip joints in pelvis radiographs and the classification of their hip dysplasia status FJ McEvoy, HF Proschowsky, AV Müller, L Moorman, J Bender‐Koch, ... Veterinary radiology & ultrasound 62 (4), 387-393, 2021 | 44 | 2021 |
| The Multivariate Generalised von Mises Distribution: Inference and Applications AKW Navarro, J Frellsen, RE Turner Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence …, 2017 | 39* | 2017 |
Wouter BoomsmaProfessor, Department of Computer Science, University of CopenhagenVerified email at di.ku.dk
