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Found 2 papers, 2 papers with code
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrograms
We propose a new machine learning anomaly detection framework for classifying both commonly occurring anomalies in radio telescopes as well as detecting unknown rare anomalies that the system has potentially not yet seen.
Bayesian Optimization for auto-tuning GPU kernels
Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise for large search spaces, even when automated.
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