Jes Frellsen

Trans. Mach. Learn. Res., 2023

Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2023

Polygonizer: An auto-regressive building delineator.

[BibT_eX]

[DOI]

Maxim Khomiakov

CoRR, 2023

Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty.

[BibT_eX]

[DOI]

CoRR, 2023

Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters.

[BibT_eX]

[DOI]

Marloes Arts

Wouter Boomsma

CoRR, 2023

Creating the next generation of news experience on ekstrabladet.dk with recommender systems.

[BibT_eX]

[DOI]

Johannes Kruse

Kasper Lindskow

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Implicit Variational Inference for High-Dimensional Posteriors.

[BibT_eX]

[DOI]

Anshuk Uppal

Kristoffer Stensbo-Smidt

Wouter Boomsma

Hugo Henri Joseph Sénétaire

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Explainability as statistical inference.

[BibT_eX]

[DOI]

Damien Garreau

Proceedings of the International Conference on Machine Learning, 2023

That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning To Generate 3d Representations of Building Roofs Using Single-View Aerial Imagery.

[BibT_eX]

[DOI]

Maxim Khomiakov

Alejandro Valverde Mahou

Alba Reinders Sánchez

Julius Holbech Radzikowski

Proceedings of the IEEE International Conference on Acoustics, 2023

Adaptive Cholesky Gaussian Processes.

[BibT_eX]

[DOI]

Simon Bartels

Kristoffer Stensbo-Smidt

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

SolarDK: A high-resolution urban solar panel image classification and localization dataset.

[BibT_eX]

[DOI]

Maxim Khomiakov

Carl Anton Schmidt

Mathias Bonde Sørensen

Mads Andersen

CoRR, 2022

deep-significance - Easy and Meaningful Statistical Significance Testing in the Age of Neural Networks.

[BibT_eX]

[DOI]

Dennis Ulmer

Christian Hardmeier

CoRR, 2022

Benchmarking Generative Latent Variable Models for Speech.

[BibT_eX]

[DOI]

CoRR, 2022

Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives.

[BibT_eX]

[DOI]

CoRR, 2022

How to deal with missing data in supervised deep learning?

[BibT_eX]

[DOI]

Niels Bruun Ipsen

Proceedings of the Tenth International Conference on Learning Representations, 2022

Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity.

[BibT_eX]

[DOI]

Dennis Ulmer

Christian Hardmeier

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Model-agnostic out-of-distribution detection using combined statistical tests.

[BibT_eX]

[DOI]

Federico Bergamin

Jakob Drachmann Havtorn

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Neural network predictions of the simulated rheological response of cement paste in the FlowCyl.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2021

Rapid Risk Minimization with Bayesian Models Through Deep Learning Approximation.

[BibT_eX]

[DOI]

CoRR, 2021

Sequential Neural Posterior and Likelihood Approximation.

[BibT_eX]

[DOI]

Samuel Wiqvist

Umberto Picchini

CoRR, 2021

Bounds all around: training energy-based models with bidirectional bounds.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Hierarchical VAEs Know What They Don't Know.

[BibT_eX]

[DOI]

Jakob Drachmann Havtorn

Søren Hauberg

Lars Maaløe

Proceedings of the 38th International Conference on Machine Learning, 2021

not-MIWAE: Deep Generative Modelling with Missing not at Random Data.

[BibT_eX]

[DOI]

Niels Bruun Ipsen

Proceedings of the 9th International Conference on Learning Representations, 2021

2019

(q, p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs.

[BibT_eX]

[DOI]

CoRR, 2019

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation.

[BibT_eX]

[DOI]

Samuel Wiqvist

Umberto Picchini

Proceedings of the 36th International Conference on Machine Learning, 2019

MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

missIWAE: Deep Generative Modelling and Imputation of Incomplete Data.

[BibT_eX]

[DOI]

CoRR, 2018

Leveraging the Exact Likelihood of Deep Latent Variable Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation.

[BibT_eX]

[DOI]

Thomas Brouwer

Pietro Liò

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Spherical convolutions and their application in molecular modelling.

[BibT_eX]

[DOI]

Wouter Boomsma

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

The Multivariate Generalised von Mises Distribution: Inference and Applications.

[BibT_eX]

[DOI]

Alexandre K. W. Navarro

Richard E. Turner

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation.

[BibT_eX]

[DOI]

Thomas Brouwer

Pietro Liò

CoRR, 2016

Bayesian Generalised Ensemble Markov Chain Monte Carlo.

[BibT_eX]

[DOI]