51 citations · 141 across the 13 of their papers we have counts for
3 papers · 1 filter
The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters
Michelle Ntampaka, A. Ciprijanovic, Ana Maria Delgado +4
The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearl…
DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
A. Ćiprijanović, D. Kafkes, K. Downey +6
In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and…
Domain adaptation techniques for improved cross-domain study of galaxy mergers
A. Ćiprijanović, D. Kafkes, S. Jenkins +5
In astronomy, neural networks are often trained on simulated data with the prospect of being applied to real observations. Unfortunately, simply training a deep neural network on i…