Следене
Jes Frellsen
Заглавие
Позовавания
Позовавания
Година
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
3562019
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
1392009
Spherical convolutions and their application in molecular modelling.
W Boomsma, J Frellsen
Advances in Neural Information Processing Systems 30 (NeurIPS 2017) 2, 6, 2017
1112017
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
1002010
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
94*2023
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
NB Ipsen, PA Mattei, J Frellsen
International Conference on Learning Representations, 2021
872021
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
832010
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
812021
Leveraging the exact likelihood of deep latent variable models
PA Mattei, J Frellsen
Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018
772018
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
622013
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
512014
How to deal with missing data in supervised deep learning?
NB Ipsen, PA Mattei, J Frellsen
International Conference on Learning Representations, 2022
482022
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
482013
Euclidean neural networks: e3nn
M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ...
Version 0.5. 0, 2022
46*2022
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
462008
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
44*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
392019
Euclidean neural networks: e3nn
M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ...
Version 0.5. 0, 2022
362022
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
342014
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
332017
Системата не може да изпълни операцията сега. Опитайте отново по-късно.
Статии 1–20