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A core assumption behind most machine learning methods is that training data should be representative for the data seen at test time.
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Stochastic optimization methods are widely used in machine learning and related fields, such as system identification and signal processing. In this talk, three such approaches are overviewed.
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Faculty of Electrical Engineering and Informatics of Budapest University of Technology and Economics and NVidia Deep Learning