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It is shown that all six estimators identify correct annotations as most informative, and are able to audit the quality of mixed text corpora, choosing the annotations that result in the best downstream forecasting results without the need for model training.
Quantum autoencoders can purify adversarial examples, boosting the robustness of quantum classifiers by up to 68% without adversarial training.