78 citations · 253 across the 56 of their papers we have counts for
9 papers · 1 filter
GeoFormer: A Multi-Polygon Segmentation Transformer
Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen
In remote sensing there exists a common need for learning scale invariant shapes of objects like buildings. Prior works relies on tweaking multiple loss functions to convert segmen…
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen
We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports high-dimensional conditional control over t…
EB-NeRD: A Large-Scale Dataset for News Recommendation
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori +6
Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challeng…
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori +6
The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender…
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate
Paul Jeha, Will Grathwohl, Michael Riis Andersen +2
Score-based models, trained with denoising score matching, are remarkably effective in generating high dimensional data. However, the high variance of their training objective hind…
Navigating Uncertainty in Medical Image Segmentation
Kilian Zepf, Jes Frellsen, Aasa Feragen
We address the selection and evaluation of uncertain segmentation methods in medical imaging and present two case studies: prostate segmentation, illustrating that for minimal anno…
Bayesian Circular Regression with von Mises Quasi-Processes
Yarden Cohen, Alexandre Khae Wu Navarro, Jes Frellsen +3
The need for regression models to predict circular values arises in many scientific fields. In this work we explore a family of expressive and interpretable distributions over circ…
Scalable physical source-to-field inference with hypernetworks
Berian James, Stefan Pollok, Ignacio Peis +3
We present a generative model that amortises computation for the field and potential around e.g.~gravitational or electromagnetic sources. Exact numerical calculation has either co…
A Continuous Relaxation for Discrete Bayesian Optimization
Richard Michael, Simon Bartels, Miguel González-Duque +4
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the object…