34 citations · 66 across the 15 of their papers we have counts for
16 papers
Inferring geometry and material properties from Mueller matrices with machine learning
Lars Doorenbos, C. H. Lucas Patty, Raphael Sznitman +1
Mueller matrices (MMs) encode information on geometry and material properties, but recovering both simultaneously is an ill-posed problem. We explore whether MMs contain sufficient…
ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
Olena Torbaniuk, Lars Doorenbos, Maurizio Paolillo +3
Modern sky surveys produce vast amounts of observational data, making the application of classical methods for estimating galaxy properties challenging and time-consuming. This cha…
Leveraging Transfer Learning for Astronomical Image Analysis
Stefano Cavuoti, Lars Doorenbos, Demetra De Cicco +7
The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibili…
Non-Linear Outlier Synthesis for Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
The reliability of supervised classifiers is severely hampered by their limitations in dealing with unexpected inputs, leading to great interest in out-of-distribution (OOD) detect…
Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is cruci…
Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
Lars Doorenbos, Eva Sextl, Kevin Heng +6
Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a ge…
Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
Deep learning models are vulnerable to performance degradation when encountering out-of-distribution (OOD) images, potentially leading to misdiagnoses and compromised patient care.…
Identification of problematic epochs in astronomical time series through transfer learning
Stefano Cavuoti, Demetra De Cicco, Lars Doorenbos +4
We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of id…
Hyperbolic Random Forests
Lars Doorenbos, Pablo Márquez-Neila, Raphael Sznitman +1
Hyperbolic space is becoming a popular choice for representing data due to the hierarchical structure - whether implicit or explicit - of many real-world datasets. Along with it co…
Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery
Alain Jungo, Lars Doorenbos, Tommaso Da Col +4
Purpose: A fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Det…
Stochastic Segmentation with Conditional Categorical Diffusion Models
Lukas Zbinden, Lars Doorenbos, Theodoros Pissas +3
Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurat…
Generating astronomical spectra from photometry with conditional diffusion models
Lars Doorenbos, Stefano Cavuoti, Giuseppe Longo +3
A trade-off between speed and information controls our understanding of astronomical objects. Fast-to-acquire photometric observations provide global properties, while costly and t…
ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
Lars Doorenbos, Olena Torbaniuk, Stefano Cavuoti +5
Modern sky surveys are producing ever larger amounts of observational data, which makes the application of classical approaches for the classification and analysis of objects chall…
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervise…
Optimising and comparing source extraction tools using objective segmentation quality criteria
Caroline Haigh, Nushkia Chamba, Aku Venhola +4
With the growth of the scale, depth, and resolution of astronomical imaging surveys, there is an increased need for highly accurate automated detection and extraction of astronomic…
Comparison of outlier detection methods on astronomical image data
Lars Doorenbos, Stefano Cavuoti, Massimo Brescia +2
Among the many challenges posed by the huge data volumes produced by the new generation of astronomical instruments there is also the search for rare and peculiar objects. Unsuperv…