Lars Doorenbos
YOU?
Author Swipe
View article: ULISSE: Determination of the star formation rate and stellar mass based on the one-shot galaxy imaging technique
ULISSE: Determination of the star formation rate and stellar mass based on the one-shot galaxy imaging technique Open
Context. Modern sky surveys produce vast amounts of observational data, which makes the application of classical methods for estimating galaxy properties challenging and time-consuming. This challenge can be significantly alleviated by emp…
View article: Leveraging Transfer Learning for Astronomical Image Analysis
Leveraging Transfer Learning for Astronomical Image Analysis Open
The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibilities offered by transfer learning techniques in addressing …
View article: Non-Linear Outlier Synthesis for Out-of-Distribution Detection
Non-Linear Outlier Synthesis for Out-of-Distribution Detection Open
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) detection. Recently, OOD detectors trained on synthetic outliers,…
View article: Identification of problematic epochs in astronomical time series through transfer learning
Identification of problematic epochs in astronomical time series through transfer learning Open
\nAims. 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 identifying and removing problematic epochs in the li…
View article: Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection
Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection Open
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 crucial for reliable deep learning models. Despite considerable …
View article: Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models Open
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 generative AI method capable of predicting optical galaxy spe…
View article: Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection
Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection Open
Deep learning models are vulnerable to performance degradation when encountering out-of-distribution (OOD) images, potentially leading to misdiagnoses and compromised patient care. These shortcomings have led to great interest in the field…
View article: Identification of problematic epochs in astronomical time series through transfer learning
Identification of problematic epochs in astronomical time series through transfer learning Open
Aims . 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 identifying and removing problematic epochs in the lig…
View article: SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants
SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants Open
In this work, we present SuFIA, the first framework for natural language-guided augmented dexterity for robotic surgical assistants. SuFIA incorporates the strong reasoning capabilities of large language models (LLMs) with perception modul…
View article: Hyperbolic Random Forests
Hyperbolic Random Forests Open
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 comes a need for algorithms capable of solving fundamental ta…
View article: Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery
Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery Open
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. Detecting so-called out-of-distribution (OoD) samples is cruci…
View article: Stochastic Segmentation with Conditional Categorical Diffusion Models
Stochastic Segmentation with Conditional Categorical Diffusion Models Open
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 accurately matches the image's content may not be suitable for saf…
View article: Generating astronomical spectra from photometry with conditional diffusion models
Generating astronomical spectra from photometry with conditional diffusion models Open
A trade-off between speed and information controls our understanding of astronomical objects. Fast-to-acquire photometric observations provide global properties, while costly and time-consuming spectroscopic measurements enable a better un…
View article: ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei
ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei Open
Context. 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 challenging and time consuming. However, this issue may…
View article: ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection Open
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 challenging and time-consuming. However, this issue may be signi…
View article: Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Data Invariants to Understand Unsupervised Out-of-Distribution Detection Open
Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD me…
View article: Optimising and comparing source-extraction tools using objective segmentation quality criteria
Optimising and comparing source-extraction tools using objective segmentation quality criteria Open
\nContext. With the growth of the scale, depth, and resolution of astronomical imaging surveys, there is increased need for highly accurate automated detection and extraction of astronomical sources from images. This also means there is a …