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Found 3 papers, 1 papers with code
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps.
Robustness of deep learning algorithms in astronomy -- galaxy morphology studies
Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data.
Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics
We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties.
Nuclear Theory Nuclear Experiment Data Analysis, Statistics and Probability
