Momentum-Based Variance Reduction in Non-Convex SGD

NeurIPS 2019 Ashok CutkoskyFrancesco Orabona

Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the converge rate of stochastic gradient descent for finding first-order critical points. However, variance reduction techniques typically require carefully tuned learning rates and willingness to use excessively large "mega-batches" in order to achieve their improved results... (read more)

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