Results 211 to 220 of about 187,984 (263)

Stochastic Poisson-embedded privacy framework for federated learning with secure homomorphic encryption in medical AI. [PDF]

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Gomathi R, Saranya K, Mahaboob John YM, Mary GLR.   +3 more
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A Stochastic Approximation Method

IEEE Transactions on Systems, Man, and Cybernetics, 1971
A new algorithm for stochastic approximation has been proposed, along with the assumptions and conditions necessary for convergence. It has been proved by two different methods that the algorithm converges to the sought value in the mean-square sense and with probability one.
Naresh K. Sinha, Michael P. Griscik
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Comments on "A Stochastic Approximation Method"

IEEE Transactions on Systems, Man, and Cybernetics, 1972
The results stated in the above paper1 concerning an approved stochastic approximation method are considered. Formulas for the variances of the estimates are derived, and it is found that, in fact, the new algorithm is inferior to previously suggested ones.
Michael A. Budin, Naresh K. Sinha
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Stochastic Methods

2020
It is clear from the previous chapters of this book that both fault-injection techniques and analytical approaches for cross-layer reliability analysis have both positive and negative aspects that must be carefully analyzed whenever choosing the best approach to evaluate the reliability of a computing system, and none of them alone represents an ...
Alessandro Savino, Alessandro Vallero, Stefano Di Carlo   +2 more
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Combining the Stochastic Counterpart and Stochastic Approximation Methods

Discrete Event Dynamic Systems, 1997
Let \(\ell(v, \theta)=E_v\{L(Y,\theta)\}\) be the expected performance of a discrete event system (DES), where \(L\) is the sample performance driven by an input vector \(Y\) with a probability density function \(f(y, v)\) and \(\theta\) is a parameter of the sample performance.
Jean-Pierre Dussault, Donald Labrecque, Pierre L'Ecuyer, Reuven Y. Rubinstein   +3 more
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Block Mirror Stochastic Gradient Method For Stochastic Optimization

Journal of Scientific Computing, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jinda Yang, Haiming Song, Xinxin Li, Di Hou   +3 more
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Stochastic methods

2022
This chapter looks into stochastic methods used to model different situations, which often involve using random numbers in one way or another. Methods like resampling and bootstrapping illustrate how flexible and widespread stochastic methods are. The main advantage of stochastic methods revolves around how it can allow working out useful stuff without
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On stochastic methods for surface reconstruction

The Visual Computer, 2007
In this article, we present and discuss three statistical methods for surface reconstruction. A typical input to a surface reconstruction technique consists of a large set of points that has been sampled from a smooth surface and contains un- certain data in the form of noise and outliers. We first present a method that filters out uncertain and redun-
Waqar Saleem, Oliver Schall, Giuseppe Patanè 0001, Alexander G. Belyaev, Hans-Peter Seidel   +4 more
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Verified stochastic methods

Soft Comput., 2013
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Gabor Rebner, Michael Beer, Ekaterina Auer, Matthias Stein   +3 more
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