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 | 278 | 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 | 133 | 2009 |
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 | 102 | 2010 |
Spherical convolutions and their application in molecular modelling. W Boomsma, J Frellsen Advances in Neural Information Processing Systems 30 (NeurIPS 2017) 2, 6, 2017 | 97 | 2017 |
Beyond rotamers: a generative, probabilistic model of side chains in proteins T Harder, W Boomsma, M Paluszewski, J Frellsen, KE Johansson, ... BMC bioinformatics 11, 1-13, 2010 | 82 | 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 | 68 | 2021 |
Leveraging the exact likelihood of deep latent variable models PA Mattei, J Frellsen Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018 | 65 | 2018 |
not-MIWAE: Deep Generative Modelling with Missing not at Random Data NB Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2021 | 60 | 2021 |
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 | 60 | 2013 |
Adaptable probabilistic mapping of short reads using position specific scoring matrices P Kerpedjiev, J Frellsen, S Lindgreen, A Krogh BMC bioinformatics 15, 1-17, 2014 | 53 | 2014 |
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation D Ulmer, C Hardmeier, J Frellsen arXiv preprint arXiv:2110.03051, 2023 | 52* | 2023 |
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 |
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 | 47 | 2013 |
How to deal with missing data in supervised deep learning? NB Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2022 | 40 | 2022 |
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 | 38 | 2019 |
Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts W Boomsma, P Tian, J Frellsen, J Ferkinghoff-Borg, T Hamelryck, ... Proceedings of the National Academy of Sciences 111 (38), 13852-13857, 2014 | 33 | 2014 |
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 | 32* | 2022 |
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 | 29 | 2017 |
Generative probabilistic models extend the scope of inferential structure determination S Olsson, W Boomsma, J Frellsen, S Bottaro, T Harder, J Ferkinghoff-Borg, ... Journal of Magnetic Resonance 213 (1), 182-186, 2011 | 25 | 2011 |
Euclidean neural networks: e3nn M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... Version 0.5. 0, 2022 | 24 | 2022 |