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This paper proposes the method of stochastic modified equations (SME) to analyze the dynamics of the SGA, and applies the framework to improve the relaxed ...
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Abstract. We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient ...
We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient algorithms, where ...
Nov 5, 2018 · We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient ...
Jun 18, 2019 · We show how the dynamics of stochastic gradient descent (SGD) is captured by a set of differential equations and prove that this description ...
One tentative explanation for the success of large networks has focused on the properties of stochastic gradient descent (SGD), the algorithm routinely used to ...
Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, ...
In this paper, We propose the method of stochastic modified equations (SME) to analyze the dynamics of the SGA. Using this technique, we can give precise ...
Stochastic gradient algorithms are widely used for large-scale learning and inference problems. However, their use in practice is.
May 21, 2024 · Is Stochastic Gradient ... We aim to show the existence of a strong equivalence between the dynamics of the two algorithms and highlight its ...