List of datasets in computer vision and image processing
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Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data). Especially in high-dimensional optimization problems this reduces the very high computational burden, achieving faster iterations in exchange for a lower convergence rate.[1]
The basic idea behind stochastic approximation can be traced back to the Robbins–Monro algorithm of the 1950s. Today, stochastic gradient descent has become an important optimization method in machine learning.[2]
^Bottou, Léon; Bousquet, Olivier (2012). "The Tradeoffs of Large Scale Learning". In Sra, Suvrit; Nowozin, Sebastian; Wright, Stephen J. (eds.). Optimization for Machine Learning. Cambridge: MIT Press. pp. 351–368. ISBN 978-0-262-01646-9.
^Bottou, Léon (1998). "Online Algorithms and Stochastic Approximations". Online Learning and Neural Networks. Cambridge University Press. ISBN 978-0-521-65263-6.
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Stochasticgradientdescent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...
of gradientdescent, stochasticgradientdescent, serves as the most basic algorithm used for training most deep networks today. Gradientdescent is based...
out-of-core versions of machine learning algorithms, for example, stochasticgradientdescent. When combined with backpropagation, this is currently the de...
of stochasticgradientdescent, where gradients are computed on a random subset of the total dataset and then used to make one step of the gradient descent...
GradientdescentStochasticgradientdescent Wolfe conditions Absil, P. A.; Mahony, R.; Andrews, B. (2005). "Convergence of the iterates of Descent methods...
can be derived through dynamic programming. Gradientdescent, or variants such as stochasticgradientdescent, are commonly used. Strictly the term backpropagation...
for all nodes in the tree. Typically, stochasticgradientdescent (SGD) is used to train the network. The gradient is computed using backpropagation through...
being stuck at local minima. One can also apply a widespread stochasticgradientdescent method with iterative projection to solve this problem. The idea...
of selection can vary with the steepness of the uphill move." Stochasticgradientdescent Russell, S.; Norvig, P. (2010). Artificial Intelligence: A Modern...
See the brief discussion in Stochasticgradientdescent. Bhatnagar, S., Prasad, H. L., and Prashanth, L. A. (2013), Stochastic Recursive Algorithms for Optimization:...
Methods of this class include: stochastic approximation (SA), by Robbins and Monro (1951) stochasticgradientdescent finite-difference SA by Kiefer and...
gradient method, generalizes the conjugate gradient method to nonlinear optimization Stochasticgradientdescent, iterative method for optimizing a differentiable...
(difference between the desired and the actual signal). It is a stochasticgradientdescent method in that the filter is only adapted based on the error...
learning, known for his work on randomized coordinate descent algorithms, stochasticgradientdescent and federated learning. He is currently a Professor...
introduced the view of boosting algorithms as iterative functional gradientdescent algorithms. That is, algorithms that optimize a cost function over...
{if}}~{\mathsf {B}}~{\textrm {wins}},\end{cases}}} and, using the stochasticgradientdescent the log loss is minimized as follows: R A ← R A − η d ℓ d R A...
prediction problems using stochasticgradientdescent algorithms. ICML. Friedman, J. H. (2001). "Greedy Function Approximation: A Gradient Boosting Machine"....
grids. If used in gradientdescent methods, random preconditioning can be viewed as an implementation of stochasticgradientdescent and can lead to faster...
|x)}}\right]} and so we obtained an unbiased estimator of the gradient, allowing stochasticgradientdescent. Since we reparametrized z {\displaystyle z} , we need...