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2020 – today
- 2024
[j8]Marloes Arts, Jes Frellsen, Wouter Boomsma:
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters. Trans. Mach. Learn. Res. 2024 (2024)- [c24]Kilian Zepf, Jes Frellsen, Aasa Feragen:
Navigating Uncertainty in Medical Image Segmentation. ISBI 2024: 1-5 - [c23]Kilian Zepf, Selma Wanna, Marco Miani, Juston Moore, Jes Frellsen, Søren Hauberg, Frederik Warburg, Aasa Feragen:
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification. MICCAI (8) 2024: 349-359 - [c22]Johannes Kruse
, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
EB-NeRD a large-scale dataset for news recommendation. RecSys Challenge 2024: 1-11 - [c21]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. RecSys 2024: 1195-1199 - [c20]Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen:
GAST: Geometry-Aware Structure Transformer. WACV (Workshops) 2024: 776-784 - [i32]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) - [i31]Berian James, Stefan Pollok, Ignacio Peis, Jes Frellsen, Rasmus Bjørk:
Scalable physical source-to-field inference with hypernetworks. CoRR abs/2405.05981 (2024) - [i30]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) - [i29]Kilian Zepf, Jes Frellsen, Aasa Feragen:
Navigating Uncertainty in Medical Image Segmentation. CoRR abs/2407.16367 (2024) - [i28]Paul Jeha, Will Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen:
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate. CoRR abs/2408.12270 (2024) - [i27]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. CoRR abs/2409.20483 (2024) - [i26]Johannes Kruse, Kasper Lindskow, Saikishore Kalloori, Marco Polignano, Claudio Pomo, Abhishek Srivastava, Anshuk Uppal, Michael Riis Andersen, Jes Frellsen:
EB-NeRD: A Large-Scale Dataset for News Recommendation. CoRR abs/2410.03432 (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) - [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 - [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
- [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 - [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
- [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) - [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 - [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)
2010 – 2019
- 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 - [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
- [c5]Pierre-Alexandre Mattei, Jes Frellsen:
Leveraging the Exact Likelihood of Deep Latent Variable Models. NeurIPS 2018: 3859-3870 - [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
- [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 - [i2]Thomas Brouwer, Jes Frellsen, Pietro Liò:
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. CoRR abs/1707.05147 (2017) - 2016
- [c1]Jes Frellsen, Ole Winther, Zoubin Ghahramani, Jesper Ferkinghoff-Borg:
Bayesian Generalised Ensemble Markov Chain Monte Carlo. AISTATS 2016: 408-416 - [i1]Thomas Brouwer, Jes Frellsen, Pietro Liò:
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation. CoRR abs/1610.08127 (2016) - 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)
2000 – 2009
- 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)
Coauthor Index
aka: Wouter K. Boomsma
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