A Probabilistic Model of RNA Conformational Space. - Jes Frellsen; Ida Moltke; Martin Thiim; Kanti V. Mardia; Jesper Ferkinghoff-Borg; Thomas Hamelryck (PLoS Comput. Biol., 2009)

Beyond rotamers: a generative, probabilistic model of side chains in proteins. - Tim Harder; Wouter Boomsma; Martin Paluszewski; Jes Frellsen; Kristoffer E. Johansson; Thomas Hamelryck (BMC Bioinform., 2010)

PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure. - 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; et al. (J. Comput. Chem., 2013)

Adaptable probabilistic mapping of short reads using position specific scoring matrices. - Peter Kerpedjiev; Jes Frellsen; Stinus Lindgreen; Anders Krogh (BMC Bioinform., 2014)

Bayesian Generalised Ensemble Markov Chain Monte Carlo. - Jes Frellsen; Ole Winther; Zoubin Ghahramani; Jesper Ferkinghoff-Borg (AISTATS, 2016)

Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation. - Thomas Brouwer; Jes Frellsen; Pietro Liò (CoRR, 2016)

The Multivariate Generalised von Mises Distribution: Inference and Applications. - Alexandre K. W. Navarro; Jes Frellsen; Richard E. Turner (AAAI, 2017)

Spherical convolutions and their application in molecular modelling. - Wouter Boomsma; Jes Frellsen (NIPS, 2017)

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation. - Thomas Brouwer; Jes Frellsen; Pietro Liò (ECML/PKDD (1), 2017)

Leveraging the Exact Likelihood of Deep Latent Variable Models. - Pierre-Alexandre Mattei; Jes Frellsen (NeurIPS, 2018)

missIWAE: Deep Generative Modelling and Imputation of Incomplete Data. - Pierre-Alexandre Mattei; Jes Frellsen (CoRR, 2018)

MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets. - Pierre-Alexandre Mattei; Jes Frellsen (ICML, 2019)

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. - Samuel Wiqvist; Pierre-Alexandre Mattei; Umberto Picchini; Jes Frellsen (ICML, 2019)

(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs. - Anton Mallasto; Jes Frellsen; Wouter Boomsma; Aasa Feragen (CoRR, 2019)

not-MIWAE: Deep Generative Modelling with Missing not at Random Data. - Niels Bruun Ipsen; Pierre-Alexandre Mattei; Jes Frellsen (ICLR, 2021)

Hierarchical VAEs Know What They Don't Know. - Jakob Drachmann Havtorn; Jes Frellsen; Søren Hauberg; Lars Maaløe (ICML, 2021)

Bounds all around: training energy-based models with bidirectional bounds. - Cong Geng; Jia Wang; Zhiyong Gao; Jes Frellsen; Søren Hauberg (NeurIPS, 2021)

Sequential Neural Posterior and Likelihood Approximation. - Samuel Wiqvist; Jes Frellsen; Umberto Picchini (CoRR, 2021)

Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation. - Mathias Löwe; Jes Frellsen; Per Lunnemann Hansen; Sebastian Risi (CoRR, 2021)

Neural network predictions of the simulated rheological response of cement paste in the FlowCyl. - Shohreh Sheiati; Navid Ranjbar; Jes Frellsen; Elisabeth L. Skare; Rolands Cepuritis; Stefan Jacobsen; Jon Spangenberg (Neural Comput. Appl., 2021)

Model-agnostic out-of-distribution detection using combined statistical tests. - Federico Bergamin; Pierre-Alexandre Mattei; Jakob Drachmann Havtorn; Hugo Sénétaire; Hugo Schmutz; Lars Maaløe; Søren Hauberg; Jes Frellsen (AISTATS, 2022)

Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity. - Dennis Ulmer; Jes Frellsen; Christian Hardmeier (EMNLP (Findings), 2022)

How to deal with missing data in supervised deep learning? - Niels Bruun Ipsen; Pierre-Alexandre Mattei; Jes Frellsen (ICLR, 2022)

Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives. - Pierre-Alexandre Mattei; Jes Frellsen (CoRR, 2022)

Benchmarking Generative Latent Variable Models for Speech. - Jakob D. Havtorn; Lasse Borgholt; Søren Hauberg; Jes Frellsen; Lars Maaløe (CoRR, 2022)

deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks. - Dennis Ulmer; Christian Hardmeier; Jes Frellsen (CoRR, 2022)

SolarDK: A high-resolution urban solar panel image classification and localization dataset. - Maxim Khomiakov; Julius Holbech Radzikowski; Carl Anton Schmidt; Mathias Bonde Sørensen; Mads Andersen; Michael Riis Andersen; Jes Frellsen (CoRR, 2022)

Adaptive Cholesky Gaussian Processes. - Simon Bartels; Kristoffer Stensbo-Smidt; Pablo Moreno-Muñoz; Wouter Boomsma; Jes Frellsen; Søren Hauberg (AISTATS, 2023)

Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery. - Maxim Khomiakov; Alejandro Valverde Mahou; Alba Reinders Sánchez; Jes Frellsen; Michael Riis Andersen (ICASSP, 2023)

That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation. - Kilian Zepf; Eike Petersen; Jes Frellsen; Aasa Feragen (ICLR, 2023)

Explainability as statistical inference. - Hugo Henri Joseph Senetaire; Damien Garreau; Jes Frellsen; Pierre-Alexandre Mattei (ICML, 2023)

Implicit Variational Inference for High-Dimensional Posteriors. - Anshuk Uppal; Kristoffer Stensbo-Smidt; Wouter Boomsma; Jes Frellsen (NeurIPS, 2023)

Creating the next generation of news experience on ekstrabladet.dk with recommender systems. - Johannes Kruse 002; Kasper Lindskow; Michael Riis Andersen; Jes Frellsen (RecSys, 2023)

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty. - Kilian Zepf; Selma Wanna; Marco Miani; Juston Moore; Jes Frellsen; Søren Hauberg; Aasa Feragen; Frederik Warburg (CoRR, 2023)

Polygonizer: An auto-regressive building delineator. - Maxim Khomiakov; Michael Riis Andersen; Jes Frellsen (CoRR, 2023)

Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition. - Simon Bartels; Wouter Boomsma; Jes Frellsen; Damien Garreau (J. Mach. Learn. Res., 2023)

Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation. - Dennis Ulmer; Christian Hardmeier; Jes Frellsen (Trans. Mach. Learn. Res., 2023)

GeoFormer: A Multi-Polygon Segmentation Transformer. - Maxim Khomiakov; Michael Riis Andersen; Jes Frellsen (BMVC, 2024)

Navigating Uncertainty in Medical Image Segmentation. - Kilian Zepf; Jes Frellsen; Aasa Feragen (ISBI, 2024)

Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification. - Kilian Zepf; Selma Wanna; Marco Miani; Juston Moore; Jes Frellsen; Søren Hauberg; Frederik Warburg; Aasa Feragen (MICCAI (8), 2024)

RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations. - Johannes Kruse 002; Kasper Lindskow; Saikishore Kalloori; Marco Polignano; Claudio Pomo; Abhishek Srivastava 004; Anshuk Uppal; Michael Riis Andersen; Jes Frellsen (RecSys, 2024)

EB-NeRD a large-scale dataset for news recommendation. - Johannes Kruse 002; Kasper Lindskow; Saikishore Kalloori; Marco Polignano; Claudio Pomo; Abhishek Srivastava 004; Anshuk Uppal; Michael Riis Andersen; Jes Frellsen (RecSys Challenge, 2024)

GAST: Geometry-Aware Structure Transformer. - Maxim Khomiakov; Michael Riis Andersen; Jes Frellsen (WACV (Workshops), 2024)

A Continuous Relaxation for Discrete Bayesian Optimization. - Richard Michael; Simon Bartels; Miguel González Duque; Yevgen Zainchkovskyy; Jes Frellsen; Søren Hauberg; Wouter Boomsma (CoRR, 2024)

Scalable physical source-to-field inference with hypernetworks. - Berian James; Stefan Pollok; Ignacio Peis; Jes Frellsen; Rasmus Bjørk (CoRR, 2024)

von Mises Quasi-Processes for Bayesian Circular Regression. - Yarden Cohen; Alexandre K. W. Navarro; Jes Frellsen; Richard E. Turner; Raziel Riemer; Ari Pakman (CoRR, 2024)

Variance reduction of diffusion model's gradients with Taylor approximation-based control variate. - Paul Jeha; Will Grathwohl; Michael Riis Andersen; Carl Henrik Ek; Jes Frellsen (CoRR, 2024)

MolMiner: Transformer architecture for fragment-based autoregressive generation of molecular stories. - Raul Ortega Ochoa; Tejs Vegge; Jes Frellsen (CoRR, 2024)

Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters. - Marloes Arts; Jes Frellsen; Wouter Boomsma (Trans. Mach. Learn. Res., 2024)

Bayesian Circular Regression with von Mises Quasi-Processes. - Yarden Cohen; Alexandre K. W. Navarro; Jes Frellsen; Richard E. Turner; Raziel Riemer; Ari Pakman (AISTATS, 2025)

Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo. - Cheuk Kit Lee; Paul Jeha; Jes Frellsen; Pietro Lio; Michael Samuel Albergo; Francisco Vargas 001 (CoRR, 2025)

Learning Energy-Based Models by Self-normalising the Likelihood. - Hugo Senetaire; Paul Jeha; Pierre-Alexandre Mattei; Jes Frellsen (CoRR, 2025)

Hyper-Transforming Latent Diffusion Models. - Ignacio Peis; Batuhan Koyuncu; Isabel Valera; Jes Frellsen (CoRR, 2025)

Exploring bidirectional bounds for minimax-training of Energy-based models. - Cong Geng; Jia Wang; Li Chen; Zhiyong Gao; Jes Frellsen; Søren Hauberg (CoRR, 2025)

Zero-shot protein stability prediction by inverse folding models: a free energy interpretation. - Jes Frellsen; Maher M. Kassem; Tone Bengtsen; Lars Olsen; Kresten Lindorff-Larsen; Jesper Ferkinghoff-Borg; Wouter Boomsma (CoRR, 2025)

Kinetic Langevin Diffusion for Crystalline Materials Generation. - François Cornet; Federico Bergamin; Arghya Bhowmik; Juan Maria Garcia Lastra; Jes Frellsen; Mikkel N. Schmidt (CoRR, 2025)