Jes Frellsen

发表

Generative probabilistic models extend the scope of inferential structure determination.

Simon Olsson, Wouter Boomsma, Jes Frellsen, 2011, Journal of magnetic resonance.

Directional Statistics in Protein Bioinformatics

Kanti V. Mardia, Jes Frellsen, Jesper Illemann Foldager, 2018, Applied Directional Statistics.

Algorithms for Mapping High-Throughput DNA Sequences**Jes Frellsen and Peter Menzel contributed equally.

Anders Krogh, Jes Frellsen, Peter Menzel, 2014 .

not-MIWAE: Deep Generative Modelling with Missing not at Random Data pdf

Jes Frellsen, Pierre-Alexandre Mattei, Niels Bruun Ipsen, 2020, ArXiv.

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

Pietro Liò, Jes Frellsen, Thomas Brouwer, 2017, ECML/PKDD.

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

Jes Frellsen, Pierre-Alexandre Mattei, Umberto Picchini, 2019, ICML.

(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs pdf

Wouter Boomsma, Jes Frellsen, Aasa Feragen, 2019, ArXiv.

On the accuracy of short read mapping.

Anders Krogh, Jes Frellsen, Peter Menzel, 2013, Methods in molecular biology.

Bayesian Generalised Ensemble Markov Chain Monte Carlo

Ole Winther, Zoubin Ghahramani, Jes Frellsen, 2016, AISTATS.

Spherical convolutions and their application in molecular modelling

Wouter Boomsma, Jes Frellsen, J. Frellsen, 2017, NIPS.

A Probabilistic Model of RNA Conformational Space

Kanti V. Mardia, Jes Frellsen, Jesper Ferkinghoff-Borg, 2009, PLoS Comput. Biol..

Probabilistic Models of Local Biomolecular Structure and Their Applications

Wouter Boomsma, Jes Frellsen, Thomas Hamelryck, 2012 .

Leveraging the Exact Likelihood of Deep Latent Variable Models

Jes Frellsen, Pierre-Alexandre Mattei, Pierre-Alexandre Mattei, 2018, NeurIPS.

MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets

Jes Frellsen, Pierre-Alexandre Mattei, Pierre-Alexandre Mattei, 2019, ICML.

missIWAE: Deep Generative Modelling and Imputation of Incomplete Data pdf

Jes Frellsen, Pierre-Alexandre Mattei, Pierre-Alexandre Mattei, 2018, ArXiv.

The Multivariate Generalised von Mises Distribution: Inference and Applications

Richard E. Turner, Jes Frellsen, Alexandre K. W. Navarro, 2016, AAAI.

Beyond rotamers: a generative, probabilistic model of side chains in proteins

Wouter Boomsma, Jes Frellsen, Tim Harder, 2010, BMC Bioinformatics.

How to deal with missing data in supervised deep learning?

Jes Frellsen, Pierre-Alexandre Mattei, Niels Bruun Ipsen, 2020, ICLR.

PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure

Kresten Lindorff-Larsen, Pengfei Tian, Jan H. Jensen, 2013, J. Comput. Chem..

Adaptable probabilistic mapping of short reads using position specific scoring matrices

Anders Krogh, Jes Frellsen, Stinus Lindgreen, 2014, BMC Bioinformatics.

Combining the multicanonical ensemble with generative probabilistic models of local biomolecular structure

Jes Frellsen, Jesper Ferkinghoff-Borg, Thomas Hamelryck, 2013 .

Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation pdf

Pietro Liò, Jes Frellsen, Thomas Brouwer, 2016, ArXiv.

Sequential Neural Posterior and Likelihood Approximation pdf

Jes Frellsen, Umberto Picchini, Samuel Wiqvist, 2021, ArXiv.

not-MIWAE: Deep Generative Modelling with Missing not at Random Data pdf

Jes Frellsen, Pierre-Alexandre Mattei, Niels Bruun Ipsen, 2020, ICLR.

Bounds all around: training energy-based models with bidirectional bounds pdf

Soren Hauberg, Jes Frellsen, Zhiyong Gao, 2021, NeurIPS.

Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives

Jes Frellsen, Pierre-Alexandre Mattei, Pierre-Alexandre Mattei, 2022, ArXiv.

Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation

Sebastian Risi, Jes Frellsen, Mathias Löwe, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition

Damien Garreau, Wouter Boomsma, Jes Frellsen, 2021, 2107.10587.

Hierarchical VAEs Know What They Don't Know pdf

Soren Hauberg, Lars Maaloe, Jes Frellsen, 2021, ICML.

Neural network predictions of the simulated rheological response of cement paste in the FlowCyl

Jes Frellsen, Stefan Jacobsen, Navid Ranjbar, 2021, Neural Computing and Applications.

Asap: A Framework for Over-Representation Statistics for Transcription Factor Binding Sites

Anders Krogh, Jes Frellsen, Ida Moltke, 2008, PloS one.

Statistics of Bivariate von Mises Distributions

Kanti V. Mardia, Jes Frellsen, K. Mardia, 2012 .

Equilibrium simulations of proteins using molecular fragment replacement and NMR chemical shifts

Kresten Lindorff-Larsen, Michele Vendruscolo, Wouter Boomsma, 2014, Proceedings of the National Academy of Sciences.

Towards a General Probabilistic Model of Protein Structure: The Reference Ratio Method

Kanti V. Mardia, Jes Frellsen, Jesper Ferkinghoff-Borg, 2012 .