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.

Shapelet model creator for SAGECal

Using DALiuGE for Distributed SAGECal with BashShellAppDrop and FileDrop

Radio interferometric calibration with PyTorch. An example of how to solve a general optimization problem.

Hint assisted reinforcement learning

Using DALiuGE for Distributed SAGECal with DynlibAppDrop and MemoryDrop