Search Results for author:
Found 5 papers, 3 papers with code
Scaling Laws for Galaxy Images
We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images.
Rapid prediction of lab-grown tissue properties using deep learning
These are used to train an implementation of the \texttt{pix2pix} deep learning model, reserving $740$ cases that were unseen in the training of the neural network for training and validation.
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch.
An old stellar population or diffuse nebular continuum emission discovered in green pea galaxies
We use new HST images of nine Green Pea Galaxies (GPGs) to study their resolved structure and color.
Astrophysics of Galaxies
Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.
