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Found 3 papers, 1 papers with code
Data-Efficient Classification of Radio Galaxies
We apply few-shot learning techniques based on Twin Networks and transfer learning techniques using a pre-trained DenseNet model with advanced techniques like cyclical learning rate and discriminative learning to train the model rapidly.
Predicting star formation properties of galaxies using deep learning
Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution.
Estimating Photometric Redshifts Using Support Vector Machines
We present a new approach to obtaining photometric redshifts using a kernel learning technique called Support Vector Machines (SVMs).
astro-ph
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