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Runtime Analysis of Stochastic Gradient Descent
Proceedings of the 4th International Conference on Computer Science and Application Engineering, 2020Stochastic gradient descent (SGD) is one of the most famous methods for minimization. However, there are few results about the runtime analysis of SGD because of its randomness. In this paper, we explain how to approximate SGD by stochastic differential equations (SDE).
Guanqiang Hu, Yushan Zhang
openaire +1 more source
2017
This chapter gives a broad overview and a historical context around the subject of deep learning. It also gives the reader a roadmap for navigating the book, the prerequisites, and further reading to dive deeper into the subject matter.
openaire +1 more source
This chapter gives a broad overview and a historical context around the subject of deep learning. It also gives the reader a roadmap for navigating the book, the prerequisites, and further reading to dive deeper into the subject matter.
openaire +1 more source
Online Covariance Matrix Estimation in Stochastic Gradient Descent
Journal of the American Statistical Association, 2023exaly
Robust Stochastic Gradient Descent With Student-t Distribution Based First-Order Momentum
IEEE Transactions on Neural Networks and Learning Systems, 2022Wendyam Eric Lionel Ilboudo +2 more
exaly
Resampling Stochastic Gradient Descent Cheaply
2023 Winter Simulation Conference (WSC), 2023Henry Lam, Zitong Wang 0005
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Risk optimization using the Chernoff bound and stochastic gradient descent
Reliability Engineering and System Safety, 2022André Torii +2 more
exaly
Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem
Stochastic Systems, 2020Konstantinos Spiliopoulos
exaly