Results 81 to 90 of about 1,273 (111)
An Improved Reacceleration Optimization Algorithm Based on the Momentum Method for Image Recognition
MathematicsThe optimization algorithm plays a crucial role in image recognition by neural networks. However, it is challenging to accelerate the model’s convergence and maintain high precision.
Haijing Sun +6 more
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Stochastic Gradient Descent on Riemannian Manifolds [PDF]
IEEE Transactions on Automatic Control, 2013 Stochastic gradient descent is a simple approach to find the local minima of a cost function whose evaluations are corrupted by noise. In this paper, we develop a procedure extending stochastic gradient descent algorithms to the case where the function is defined on a Riemannian manifold.
Silvere Bonnabel
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Preconditioned Stochastic Gradient Descent [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 13 pages, 9 figures. To appear in IEEE Transactions on Neural Networks and Learning Systems.
Xi-Lin Li
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A stochastic multiple gradient descent algorithm [PDF]
European Journal of Operational Research, 2018 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fabrice Poirion +2 more
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On the different regimes of stochastic gradient descent
Proceedings of the National Academy of Sciences of the United States of AmericaModern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size B , and the step size or learning rate
Antonio Sclocchi, Matthieu Wyart
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Backpropagation and stochastic gradient descent method
Neurocomputing, 1993 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shun'ichi Amari
exaly +5 more sources
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
openaire +2 more sources
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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