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Stochastic Gradient Descent with GPGPU
2012We show how to optimize a Support Vector Machine and a predictor for Collaborative Filtering with Stochastic Gradient Descent on the GPU, achieving 1.66 to 6-times accelerations compared to a CPU-based implementation. The reference implementations are the Support Vector Machine by Bottou and the BRISMF predictor from the Netflix Prices winning team ...
David Zastrau, Stefan Edelkamp
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Stochastic gradient descent possibilistic clustering
11th Hellenic Conference on Artificial Intelligence, 2020Although online versions of several well known clustering algorithms have been proposed, in order to deal effectively with the big data issue, as well as with the case where the data are available in a streaming fashion, very few of them follow the stochastic gradient descent philosophy.
Aggeliki Koutsibella +1 more
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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
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Conjugate Directions for Stochastic Gradient Descent
2002The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to stochastic approximation of the gradient. Here we explore ideas from conjugate gradient in the stochastic (online) setting, using fast Hessian-gradient products to set up low ...
Nicol N. Schraudolph, Thore Graepel
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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.
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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
Resampling Stochastic Gradient Descent Cheaply
2023 Winter Simulation Conference (WSC), 2023Henry Lam, Zitong Wang 0005
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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