Jes Frellsen

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 53

Top Topics

Latent Variable Models

Gaussian Process

Top Venues

Publications

Kilian Zepf, Jes Frellsen, Aasa Feragen
Navigating Uncertainty in Medical Image Segmentation. CoRR (2024)
Marloes Arts, Jes Frellsen, Wouter Boomsma
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters. Trans. Mach. Learn. Res. 2024 (2024)
Richard Michael, Simon Bartels, Miguel González Duque, Yevgen Zainchkovskyy, Jes Frellsen, Søren Hauberg, Wouter Boomsma
A Continuous Relaxation for Discrete Bayesian Optimization. CoRR (2024)
Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman
von Mises Quasi-Processes for Bayesian Circular Regression. CoRR (2024)
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
GAST: Geometry-Aware Structure Transformer. WACV (Workshops) (2024)
Berian James, Stefan Pollok, Ignacio Peis, Jes Frellsen, Rasmus Bjørk
Scalable physical source-to-field inference with hypernetworks. CoRR (2024)
Hugo Henri Joseph Sénétaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei
Explainability as statistical inference. ICML (2023)
Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition. J. Mach. Learn. Res. 24 (2023)
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)
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 (2023)
Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter Boomsma, Jes Frellsen
Implicit Variational Inference for High-Dimensional Posteriors. NeurIPS (2023)
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg
Adaptive Cholesky Gaussian Processes. AISTATS (2023)
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)
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 (2023)
Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen
That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation. CoRR (2023)
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen, Jes Frellsen
Creating the next generation of news experience on ekstrabladet.dk with recommender systems. RecSys (2023)
Marloes Arts, Jes Frellsen, Wouter Boomsma
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters. CoRR (2023)
Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter K. Boomsma, Jes Frellsen
Implicit Variational Inference for High-Dimensional Posteriors. CoRR (2023)
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
Polygonizer: An auto-regressive building delineator. CoRR (2023)
Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen
That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation. ICLR (2023)
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg
Adaptive Cholesky Gaussian Processes. CoRR (2022)
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)
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 (2022)
Jakob D. Havtorn, Lasse Borgholt, Søren Hauberg, Jes Frellsen, Lars Maaløe
Benchmarking Generative Latent Variable Models for Speech. CoRR (2022)
Pierre-Alexandre Mattei, Jes Frellsen
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives. CoRR (2022)
Dennis Ulmer, Christian Hardmeier, Jes Frellsen
deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks. CoRR (2022)
Dennis Ulmer, Jes Frellsen, Christian Hardmeier
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity. CoRR (2022)
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
How to deal with missing data in supervised deep learning? ICLR (2022)
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn, Hugo Senetaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, Jes Frellsen
Model-agnostic out-of-distribution detection using combined statistical tests. CoRR (2022)
Hugo Henri Joseph Sénétaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei
Explainability as statistical inference. CoRR (2022)
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)
Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg
Bounds all around: training energy-based models with bidirectional bounds. NeurIPS (2021)
Jakob Drachmann Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe
Hierarchical VAEs Know What They Don't Know. ICML (2021)
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
not-MIWAE: Deep Generative Modelling with Missing not at Random Data. ICLR (2021)
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) (2021)
Samuel Wiqvist, Jes Frellsen, Umberto Picchini
Sequential Neural Posterior and Likelihood Approximation. CoRR (2021)
Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg
Bounds all around: training energy-based models with bidirectional bounds. CoRR (2021)
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe
Hierarchical VAEs Know What They Don't Know. CoRR (2021)
Mathias Löwe, Jes Frellsen, Per Lunnemann Hansen, Sebastian Risi
Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation. CoRR (2021)
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
not-MIWAE: Deep Generative Modelling with Missing not at Random Data. CoRR (2020)
Pierre-Alexandre Mattei, Jes Frellsen
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets. ICML (2019)
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. CoRR (2019)
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. ICML (2019)
Anton Mallasto, Jes Frellsen, Wouter Boomsma, Aasa Feragen
(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs. CoRR (2019)
Pierre-Alexandre Mattei, Jes Frellsen
Leveraging the Exact Likelihood of Deep Latent Variable Models. NeurIPS (2018)
Pierre-Alexandre Mattei, Jes Frellsen
Leveraging the Exact Likelihood of Deep Latent Variable Models. CoRR (2018)
Pierre-Alexandre Mattei, Jes Frellsen
missIWAE: Deep Generative Modelling and Imputation of Incomplete Data. CoRR (2018)
Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner
The Multivariate Generalised von Mises Distribution: Inference and Applications. AAAI (2017)
Thomas Brouwer, Jes Frellsen, Pietro Liò
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. CoRR (2017)
Thomas Brouwer, Jes Frellsen, Pietro Liò
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. ECML/PKDD (1) (2017)
Wouter Boomsma, Jes Frellsen
Spherical convolutions and their application in molecular modelling. NIPS (2017)
Thomas Brouwer, Jes Frellsen, Pietro Liò
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation. CoRR (2016)
Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg
Bayesian Generalised Ensemble Markov Chain Monte Carlo. AISTATS (2016)
Peter Kerpedjiev, Jes Frellsen, Stinus Lindgreen, Anders Krogh
Adaptable probabilistic mapping of short reads using position specific scoring matrices. BMC Bioinform. 15 (2014)
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) (2013)
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 (2010)
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)