Fast Probabilistic Optimization from Noisy Gradients Philipp Hennig philipp
hennigtuebingenmpgde Max Planck Institute for Intelligent Systems Dpt of Empirical Inference Spemannstr Tubingen Germany Abstract Stochastic gradient descent remains popular in largescale machine learning on account of its very low computational cost
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