78 citations · 253 across the 56 of their papers we have counts for
6 papers · 1 filter
Implicit Variational Inference for High-Dimensional Posteriors
Anshuk Uppal, Kristoffer Stensbo-Smidt, Wouter Boomsma +1
In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distr…
Polygonizer: An auto-regressive building delineator
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
In geospatial planning, it is often essential to represent objects in a vectorized format, as this format easily translates to downstream tasks such as web development, graphics, o…
That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation
Kilian Zepf, Eike Petersen, Jes Frellsen +1
Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, i…
Learning to Generate 3D Representations of Building Roofs Using Single-View Aerial Imagery
Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez +2
We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a s…
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
Kilian Zepf, Selma Wanna, Marco Miani +5
Image segmentation relies heavily on neural networks which are known to be overconfident, especially when making predictions on out-of-distribution (OOD) images. This is a common s…
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters
Marloes Arts, Jes Frellsen, Wouter Boomsma
After the recent ground-breaking advances in protein structure prediction, one of the remaining challenges in protein machine learning is to reliably predict distributions of struc…