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Jes Frellsen
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Journal Articles
- 2024
- [j8]Marloes Arts, Jes Frellsen, Wouter Boomsma:
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j7]Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau:
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition. J. Mach. Learn. Res. 24: 71:1-71:57 (2023) - [j6]Dennis Ulmer, Christian Hardmeier, Jes Frellsen:
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation. Trans. Mach. Learn. Res. 2023 (2023) - 2021
- [j5]Shohreh Sheiati, Navid Ranjbar
, Jes Frellsen
, Elisabeth L. Skare, Rolands Cepuritis, Stefan Jacobsen, Jon Spangenberg
:
Neural network predictions of the simulated rheological response of cement paste in the FlowCyl. Neural Comput. Appl. 33(19): 13027-13037 (2021) - 2014
- [j4]Peter Kerpedjiev
, Jes Frellsen
, Stinus Lindgreen, Anders Krogh
:
Adaptable probabilistic mapping of short reads using position specific scoring matrices. BMC Bioinform. 15: 100 (2014) - 2013
- [j3]Wouter Boomsma
, Jes Frellsen
, Tim Harder, Sandro Bottaro
, Kristoffer E. Johansson
, Pengfei Tian
, Kasper Stovgaard, Christian Andreetta, Simon Olsson
, Jan B. Valentin
, Lubomir D. Antonov
, Anders S. Christensen, Mikael Borg
, Jan H. Jensen
, Kresten Lindorff-Larsen
, Jesper Ferkinghoff-Borg, Thomas Hamelryck
:
PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure. J. Comput. Chem. 34(19): 1697-1705 (2013) - 2010
- [j2]Tim Harder, Wouter Boomsma
, Martin Paluszewski, Jes Frellsen
, Kristoffer E. Johansson
, Thomas Hamelryck
:
Beyond rotamers: a generative, probabilistic model of side chains in proteins. BMC Bioinform. 11: 306 (2010) - 2009
- [j1]Jes Frellsen
, Ida Moltke
, Martin Thiim, Kanti V. Mardia, Jesper Ferkinghoff-Borg, Thomas Hamelryck
:
A Probabilistic Model of RNA Conformational Space. PLoS Comput. Biol. 5(6) (2009)
Conference and Workshop Papers
- 2024
- [c20]Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen:
GAST: Geometry-Aware Structure Transformer. WACV (Workshops) 2024: 776-784 - 2023
- [c19]Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg:
Adaptive Cholesky Gaussian Processes. AISTATS 2023: 408-452 - [c18]Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen
, Michael Riis Andersen:
Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery. ICASSP 2023: 1-5 - [c17]Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen:
That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation. ICLR 2023 - [c16]Hugo Henri Joseph Sénétaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei:
Explainability as statistical inference. ICML 2023: 30584-30612 - [c15]Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter Boomsma, Jes Frellsen:
Implicit Variational Inference for High-Dimensional Posteriors. NeurIPS 2023 - [c14]Johannes Kruse
, Kasper Lindskow
, Michael Riis Andersen
, Jes Frellsen
:
Creating the next generation of news experience on ekstrabladet.dk with recommender systems. RecSys 2023: 1067-1070 - 2022
- [c13]Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen:
Model-agnostic out-of-distribution detection using combined statistical tests. AISTATS 2022: 10753-10776 - [c12]Dennis Ulmer, Jes Frellsen, Christian Hardmeier:
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity. EMNLP (Findings) 2022: 2707-2735 - [c11]Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen:
How to deal with missing data in supervised deep learning? ICLR 2022 - 2021
- [c10]Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen:
not-MIWAE: Deep Generative Modelling with Missing not at Random Data. ICLR 2021 - [c9]Jakob Drachmann Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe:
Hierarchical VAEs Know What They Don't Know. ICML 2021: 4117-4128 - [c8]Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg:
Bounds all around: training energy-based models with bidirectional bounds. NeurIPS 2021: 19808-19821 - 2019
- [c7]Pierre-Alexandre Mattei, Jes Frellsen
:
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets. ICML 2019: 4413-4423 - [c6]Samuel Wiqvist
, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen
:
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. ICML 2019: 6798-6807 - 2018
- [c5]Pierre-Alexandre Mattei, Jes Frellsen:
Leveraging the Exact Likelihood of Deep Latent Variable Models. NeurIPS 2018: 3859-3870 - 2017
- [c4]Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner:
The Multivariate Generalised von Mises Distribution: Inference and Applications. AAAI 2017: 2394-2400 - [c3]Wouter Boomsma, Jes Frellsen:
Spherical convolutions and their application in molecular modelling. NIPS 2017: 3433-3443 - [c2]Thomas Brouwer
, Jes Frellsen
, Pietro Liò
:
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. ECML/PKDD (1) 2017: 513-529 - 2016
- [c1]Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg:
Bayesian Generalised Ensemble Markov Chain Monte Carlo. AISTATS 2016: 408-416
Informal and Other Publications
- 2024
- [i29]Richard Michael, Simon Bartels, Miguel González Duque, Yevgen Zainchkovskyy, Jes Frellsen, Søren Hauberg, Wouter Boomsma:
A Continuous Relaxation for Discrete Bayesian Optimization. CoRR abs/2404.17452 (2024) - [i28]Berian James, Stefan Pollok, Ignacio Peis, Jes Frellsen, Rasmus Bjørk:
Scalable physical source-to-field inference with hypernetworks. CoRR abs/2405.05981 (2024) - [i27]Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman:
von Mises Quasi-Processes for Bayesian Circular Regression. CoRR abs/2406.13151 (2024) - [i26]Kilian Zepf, Jes Frellsen, Aasa Feragen:
Navigating Uncertainty in Medical Image Segmentation. CoRR abs/2407.16367 (2024) - 2023
- [i25]Marloes Arts, Jes Frellsen, Wouter Boomsma:
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters. CoRR abs/2302.13711 (2023) - [i24]Maxim Khomiakov
, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen, Michael Riis Andersen:
Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery. CoRR abs/2303.11215 (2023) - [i23]Kilian Zepf, Selma Wanna
, Marco Miani, Juston Moore, Jes Frellsen, Søren Hauberg, Aasa Feragen
, Frederik Warburg:
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty. CoRR abs/2303.13123 (2023) - [i22]Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen
:
That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation. CoRR abs/2303.15850 (2023) - [i21]Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen:
Polygonizer: An auto-regressive building delineator. CoRR abs/2304.04048 (2023) - [i20]Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter K. Boomsma, Jes Frellsen:
Implicit Variational Inference for High-Dimensional Posteriors. CoRR abs/2310.06643 (2023) - 2022
- [i19]Pierre-Alexandre Mattei, Jes Frellsen:
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives. CoRR abs/2201.10989 (2022) - [i18]Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg:
Adaptive Cholesky Gaussian Processes. CoRR abs/2202.10769 (2022) - [i17]Jakob D. Havtorn
, Lasse Borgholt, Søren Hauberg, Jes Frellsen, Lars Maaløe:
Benchmarking Generative Latent Variable Models for Speech. CoRR abs/2202.12707 (2022) - [i16]Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen:
Model-agnostic out-of-distribution detection using combined statistical tests. CoRR abs/2203.01097 (2022) - [i15]Dennis Ulmer, Christian Hardmeier
, Jes Frellsen:
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks. CoRR abs/2204.06815 (2022) - [i14]Dennis Ulmer, Jes Frellsen, Christian Hardmeier
:
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity. CoRR abs/2210.15452 (2022) - [i13]Maxim Khomiakov
, Julius Holbech Radzikowski, Carl Anton Schmidt, Mathias Bonde Sørensen, Mads Andersen, Michael Riis Andersen, Jes Frellsen:
SolarDK: A high-resolution urban solar panel image classification and localization dataset. CoRR abs/2212.01260 (2022) - [i12]Hugo Henri Joseph Sénétaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei:
Explainability as statistical inference. CoRR abs/2212.03131 (2022) - 2021
- [i11]Samuel Wiqvist
, Jes Frellsen, Umberto Picchini:
Sequential Neural Posterior and Likelihood Approximation. CoRR abs/2102.06522 (2021) - [i10]Jakob D. Havtorn
, Jes Frellsen, Søren Hauberg, Lars Maaløe:
Hierarchical VAEs Know What They Don't Know. CoRR abs/2102.08248 (2021) - [i9]Mathias Löwe
, Jes Frellsen, Per Lunnemann Hansen, Sebastian Risi:
Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation. CoRR abs/2103.15682 (2021) - [i8]Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg:
Bounds all around: training energy-based models with bidirectional bounds. CoRR abs/2111.00929 (2021) - 2020
- [i7]Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen:
not-MIWAE: Deep Generative Modelling with Missing not at Random Data. CoRR abs/2006.12871 (2020) - 2019
- [i6]Samuel Wiqvist
, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen:
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. CoRR abs/1901.10230 (2019) - [i5]Anton Mallasto, Jes Frellsen
, Wouter Boomsma, Aasa Feragen:
(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs. CoRR abs/1902.03642 (2019) - 2018
- [i4]Pierre-Alexandre Mattei, Jes Frellsen:
Leveraging the Exact Likelihood of Deep Latent Variable Models. CoRR abs/1802.04826 (2018) - [i3]Pierre-Alexandre Mattei, Jes Frellsen:
missIWAE: Deep Generative Modelling and Imputation of Incomplete Data. CoRR abs/1812.02633 (2018) - 2017
- [i2]Thomas Brouwer, Jes Frellsen, Pietro Liò:
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. CoRR abs/1707.05147 (2017) - 2016
- [i1]Thomas Brouwer, Jes Frellsen
, Pietro Liò:
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation. CoRR abs/1610.08127 (2016)
Coauthor Index
aka: Wouter K. Boomsma
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last updated on 2024-08-20 20:29 CEST by the dblp team
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