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Test Selection for Deep Learning Systems
This raises the question of how we can automatically select candidate test data to test deep learning models.
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window.
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