Federated learning with PyTorch (federated averaging and consensus optimization): with 'reduced' bandwidth
SAGECal is a fast, memory efficient and GPU accelerated radio interferometric calibration program. It supports all source models including points, Gaussians and Shapelets. Distributed calibration using MPI and consensus optimization is enabled. Both spectral and spatial priors can be used as constraints. Tools to build/restore sky models are inc…
Deep reinforcement learning for smart calibration of radio telescopes. Automatic hyper-parameter tuning.
Hint assisted reinforcement learning
Shapelet model creator for SAGECal
Radio interferometric calibration with PyTorch. An example of how to solve a general optimization problem.
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