Frellsen, Jes

30 publications

TMLR 2026 Scalable Physical Source-to-Field Inference with Hypernetworks Berian James, Stefan Pollok, Ignacio Peis, Elizabeth Louise Baker, Jes Frellsen, Rasmus Bjørk

AISTATS 2025 Bayesian Circular Regression with Von Mises Quasi-Processes Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

ICLRW 2025 Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo Lee Cheuk Kit, Paul Jeha, Jes Frellsen, Pietro Lio, Michael Samuel Albergo, Francisco Vargas

ICML 2025 Hyper-Transforming Latent Diffusion Models Ignacio Peis, Batuhan Koyuncu, Isabel Valera, Jes Frellsen

ICML 2025 Kinetic Langevin Diffusion for Crystalline Materials Generation François R J Cornet, Federico Bergamin, Arghya Bhowmik, Juan Maria Garcia-Lastra, Jes Frellsen, Mikkel N. Schmidt

ICLRW 2025 Kinetic Langevin Diffusion for Crystalline Materials Generation François R J Cornet, Federico Bergamin, Arghya Bhowmik, Juan Maria Garcia-Lastra, Jes Frellsen, Mikkel N. Schmidt

NeurIPS 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

TMLR 2024 Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters Marloes Arts, Jes Frellsen, Wouter Boomsma

ICMLW 2024 Variance Reduction of Diffusion Model's Gradients with Taylor Approximation-Based Control Variate Paul Jeha, Will Sussman Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen

ICMLW 2024 Von Mises Quasi-Processes for Bayesian Circular Regression Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

AISTATS 2023 Adaptive Cholesky Gaussian Processes Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Munoz, Wouter Boomsma, Jes Frellsen, Soren Hauberg

ICML 2023 Explainability as Statistical Inference Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen, Pierre-Alexandre Mattei

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

JMLR 2023 Kernel-Matrix Determinant Estimates from Stopped Cholesky Decomposition Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau

TMLR 2023 Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods for Uncertainty Estimation Dennis Thomas Ulmer, Christian Hardmeier, Jes Frellsen

ICLR 2023 That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation Kilian Zepf, Eike Petersen, Jes Frellsen, Aasa Feragen

AISTATS 2022 Model-Agnostic Out-of-Distribution Detection Using Combined Statistical Tests Federico Bergamin, Pierre-Alexandre Mattei, Jakob Drachmann Havtorn, Hugo Sénétaire, Hugo Schmutz, Lars Maaløe, Soren Hauberg, Jes Frellsen

ICLRW 2022 Benchmarking Generative Latent Variable Models for Speech Jakob Drachmann Havtorn, Lasse Borgholt, Søren Hauberg, Jes Frellsen, Lars Maaløe

ICLR 2022 How to Deal with Missing Data in Supervised Deep Learning? Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

NeurIPS 2021 Bounds All Around: Training Energy-Based Models with Bidirectional Bounds Cong Geng, Jia Wang, Zhiyong Gao, Jes Frellsen, Søren Hauberg

ICML 2021 Hierarchical VAEs Know What They Don’t Know Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe

ICLR 2021 Not-MIWAE: Deep Generative Modelling with Missing Not at Random Data Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

ICMLW 2020 How to Deal with Missing Data in Supervised Deep Learning? Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

ICML 2019 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

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

ECML-PKDD 2017 Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation Thomas Brouwer, Jes Frellsen, Pietro Liò

NeurIPS 2017 Spherical Convolutions and Their Application in Molecular Modelling Wouter Boomsma, Jes Frellsen

AAAI 2017 The Multivariate Generalised Von Mises Distribution: Inference and Applications Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner

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