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Jes Frellsen
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Year
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
4502019
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
1412009
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
1192017
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
NB Ipsen, PA Mattei, J Frellsen
International Conference on Learning Representations, 2021
1112021
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
1092010
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
982021
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
842010
Leveraging the exact likelihood of deep latent variable models
PA Mattei, J Frellsen
Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018
822018
How to deal with missing data in supervised deep learning?
NB Ipsen, PA Mattei, J Frellsen
International Conference on Learning Representations, 2022
672022
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
632013
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
512022
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
512014
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
492013
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
482008
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
462019
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
442021
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
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Articles 1–20