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Found 5 papers, 4 papers with code
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS).
Neural network accelerator for quantum control
Efficient quantum control is necessary for practical quantum computing implementations with current technologies.
Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning.
Accelerator Physics
Inferring Convolutional Neural Networks' accuracies from their architectural characterizations
We characterize CNN's architecture by different attributes, and demonstrate that the attributes can be predictive of the networks' performance in two specific computer vision-based physics problems -- event vertex finding and hadron multiplicity classification in the MINERvA experiment at Fermi National Accelerator Laboratory.
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer
We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems.
