47 citations · 60 across the 6 of their papers we have counts for
6 papers
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
A. Ćiprijanović, A. Lewis, K. Pedro +4
Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to t…
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
Aleksandra Ćiprijanović, Ashia Lewis, Kevin Pedro +4
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discove…
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder +6
With increased adoption of supervised deep learning methods for processing and analysis of cosmological survey data, the assessment of data perturbation effects (that can naturally…
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
A. Ćiprijanović, D. Kafkes, G. N. Perdue +6
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these…
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…