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 ...

We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient algorithms, where ...

In this paper, we developed the general mathematical foundation of the stochastic modified equations framework for analyzing stochastic gradient algorithms.

Nov 5, 2018 · Title:Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations ; Subjects: Machine Learning (cs.

We show how the dynamics of stochastic gradient descent (SGD) is captured by a set of differential equations and prove that this description is asymptotically ...

Jun 18, 2019 · Abstract:Deep neural networks achieve stellar generalisation even when they have enough parameters to easily fit all their training data.

Stochastic gradient algorithms (SGA) are increasingly popular in machine learning applications and have become "the algorithm" for extremely large scale ...

Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins– ...

Abstract. In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches.

Mar 14, 2024 · Gradient Descent is an iterative optimization process that searches for an objective function's optimum value (Minimum/Maximum).