Galaxy morphology reflects structural properties that contribute to the understanding of the
formation and evolution of galaxies. Deep convolutional networks have proven to be very …

Graph neural networks (GNNs) build on the success of deep learning models by extending
them for use in graph spaces. Transfer learning has proven extremely successful for …

We present a machine-learning framework to accurately characterize the morphologies of
active galactic nucleus (AGN) host galaxies within z< 1. We first use PSFGAN to decouple …

Context-aware recommendation systems improve upon classical recommender systems by
including, in the modelling, a user's behaviour. Research into context-aware …

In the field of astronomy, it is essential to classify celestial objects like stars, galaxies, and
quasars based on their spectral characteristics. This spectral data provides valuable …

Graph neural networks (GNNs) build on the success of deep learning models by extending
them for use in graph spaces. Transfer learning has proven extremely successful for …

Automated re-identification of individuals in endangered species has gained traction in
nature conservation initiatives. Scholars in computer vision community have explored the …

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data
relationship. It is critical in many machine learning, pattern recognition and data mining …