204 citations · 636 across the 40 of their papers we have counts for
42 papers
Likelihood-ratio-based confidence intervals for neural networks
Laurens Sluijterman, Eric Cator, Tom Heskes
This paper introduces a first implementation of a novel likelihood-ratio-based approach for constructing confidence intervals for neural networks. Our method, called DeepLR, offers…
AutoSourceID-Classifier. Star-Galaxy Classification using a Convolutional Neural Network with Spatial Information
F. Stoppa, S. Bhattacharyya, R. Ruiz de Austri +10
Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogues, a process susceptible to introducing inaccuracies, thereby potentially jeo…
Random walks on with metastable Gaussian distribution caused by linear drift with application to the contact process on the complete graph
O. S. Awolude, E. Cator, H. Don
We study random walks on which have a linear (or almost linear) drift towards 0 in a range around 0. This drift leads to a metastable Gaussian distribution centered at…
AutoSourceID-FeatureExtractor. Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation
F. Stoppa, R. Ruiz de Austri, P. Vreeswijk +9
Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regress…
Consistency Tests for Comparing Astrophysical Models and Observations
Fiorenzo Stoppa, Eric Cator, Gijs Nelemans
In astronomy, there is an opportunity to enhance the practice of validating models through statistical techniques, specifically to account for measurement error uncertainties. Whil…
Optimal Training of Mean Variance Estimation Neural Networks
Laurens Sluijterman, Eric Cator, Tom Heskes
This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building bloc…