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Towards machines that know when they do not know: Summary of work done at 2014 Frederick Jelinek Memorial Workshop

A group of junior and senior researchers gathered as a part of the 2014 Frederick Jelinek Memorial Workshop in Prague to address the problem of predicting the accuracy of a nonlinear Deep NeuralNetwork probability estimator for unknown data in a different application domain from the domain in which the estimator was trained. The paper describes the problem and summarizes approaches that were taken by the group1

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