diffusion approximations

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Study investigates diffusion approximations of stochastic gradient descent

A preprint examines the use of diffusion approximations, which substitute Gaussian noise for the randomness of stochastic gradients, as a way to analyze how stochastic gradient descent behaves over time. The authors probe the conditions under which such approximations remain valid for describing the algorithm's long-run dynamics.

sgd/optimization/theory/arxiv