Results 21 to 30 of about 142,362 (315)

Graph Drawing by Stochastic Gradient Descent [PDF]

IEEE Transactions on Visualization and Computer Graphics, 2019
Submitted to IEEE Transactions on Visualization and Computer Graphics on 26/06 ...
Jonathan X. Zheng, Samraat Pawar, Dan F. M. Goodman   +2 more
openaire   +5 more sources

Natural Evolutionary Gradient Descent Strategy for Variational Quantum Algorithms

Intelligent Computing, 2023
Recent research has demonstrated that parametric quantum circuits (PQCs) are affected by gradients that progressively vanish to zero as a function of the number of qubits.
Jianshe Xie, Chen Xu, Chenhao Yin, Yumin Dong, Zhirong Zhang   +4 more
doaj   +1 more source

A dual enhanced stochastic gradient descent method with dynamic momentum and step size adaptation for improved optimization performance. [PDF]

Sci Rep
Mokhtar MA, Fathy M, Dahab YA, Sayed EA.
europepmc   +3 more sources

Stochastic Gradient Descent in Continuous Time [PDF]

SSRN Electronic Journal, 2017
Stochastic gradient descent in continuous time (SGDCT) provides a computationally efficient method for the statistical learning of continuous-time models, which are widely used in science, engineering, and finance. The SGDCT algorithm follows a (noisy) descent direction along a continuous stream of data.
Sirignano, Justin, Spiliopoulos, Konstantinos   +1 more
openaire   +2 more sources

Featured Hybrid Recommendation System Using Stochastic Gradient Descent

International Journal of Networked and Distributed Computing (IJNDC), 2021
Beside cold-start and sparsity, developing incremental algorithms emerge as interesting research to recommendation system in real-data environment. While hybrid system research is insufficient due to the complexity in combining various source of each ...
Si Thin Nguyen, Hyun Young Kwak, Si Young Lee, Gwang Yong Gim   +3 more
doaj   +1 more source

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC [PDF]

, 2015
Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference.
Adams R., Ahn S., Ahn S., Bardenet R., Bennett J., Bezanson J., Chen T., Ding N., Dror G., Girolami M., Hall K. B., Korattikara A., Mann G., McDonald R., Mnih A., Neal R., Patterson S., Porteous I., Rossky P., Welling M., Zinkevich M.   +20 more
core   +2 more sources

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.
openaire   +3 more sources

Adaptive Gradient Estimation Stochastic Parallel Gradient Descent Algorithm for Laser Beam Cleanup

Photonics, 2021
For a high-power slab solid-state laser, obtaining high output power and high output beam quality are the most important indicators. Adaptive optics systems can significantly improve beam qualities by compensating for the phase distortions of the laser ...
Shiqing Ma, Ping Yang, Boheng Lai, Chunxuan Su, Wang Zhao, Kangjian Yang, Ruiyan Jin, Tao Cheng, Bing Xu   +8 more
doaj   +1 more source

Stochastic Reweighted Gradient Descent

, 2021
Despite the strong theoretical guarantees that variance-reduced finite-sum optimization algorithms enjoy, their applicability remains limited to cases where the memory overhead they introduce (SAG/SAGA), or the periodic full gradient computation they require (SVRG/SARAH) are manageable.
Hanchi, Ayoub El, Stephens, David A.
openaire   +2 more sources

Granular Elastic Network Regression with Stochastic Gradient Descent

Mathematics, 2022
Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine.
Linjie He, Yumin Chen, Caiming Zhong, Keshou Wu   +3 more
doaj   +1 more source