Results 101 to 110 of about 1,424,164 (189)

Scaling of hardware-compatible perturbative training algorithms

APL Machine Learning
In this work, we explore the capabilities of multiplexed gradient descent (MGD), a scalable and efficient perturbative zeroth-order training method for estimating the gradient of a loss function in hardware and training it via stochastic gradient descent.
B. G. Oripov, A. Dienstfrey, A. N. McCaughan, S. M. Buckley   +3 more
doaj   +1 more source

Stochastic Gradient Descent with Adaptive Data

Operations Research
Stochastic Gradient Descent with Adaptive Data Stochastic gradient descent (SGD) is a central tool in modern optimization, but its classical theory relies on the assumption that data are independent of the decisions being optimized. In many operations research settings, this assumption fails: policies influence system dynamics, and ...
Ethan Che, Jing Dong, Xin T. Tong
openaire   +3 more sources

A novel deep learning technique for medical image analysis using improved optimizer

Health Informatics Journal
Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth.
Vertika Agarwal, MC Lohani, Ankur Singh Bist   +2 more
doaj   +1 more source

Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation. [PDF]

Open Life Sci, 2023
Nagendram S, Singh A, Harish Babu G, Joshi R, Pande SD, Ahammad SKH, Dhabliya D, Bisht A.   +7 more
europepmc   +1 more source

A SIEVE STOCHASTIC GRADIENT DESCENT ESTIMATOR FOR ONLINE NONPARAMETRIC REGRESSION IN SOBOLEV ELLIPSOIDS. [PDF]

Ann Stat, 2022
Zhang T, Simon N.
europepmc   +1 more source

Matrix Factorization based Collaborative Filtering with Resilient Stochastic Gradient Descent

, 2018
One of the leading approaches to collaborative filtering is to use matrix factorization to discover a set of latent factors that explain the pattern of preferences.
Islam Elnabarawy, Abdelbar, Ashraf M., Khalid M. Salama, Donald C. Wunsch, Ashraf M. Abdelbar, Elnabarawy, Islam, Wunsch, Donald C., Salama, Khalid M.   +7 more
core   +1 more source

Random coordinate descent: A simple alternative for optimizing parameterized quantum circuits

Physical Review Research
Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of ...
Zhiyan Ding, Taehee Ko, Jiahao Yao, Lin Lin, Xiantao Li   +4 more
doaj   +1 more source

Performance Analysis of an Optical System for FSO Communications Utilizing Combined Stochastic Gradient Descent Optimization Algorithm

Applied System Innovation
Wavefront aberrations caused by thermal flows or arising from the quality of optical components can significantly impair wireless communication links. Such aberrations may result in an increased error rate in the received signal, leading to data loss in ...
Ilya Galaktionov, Vladimir Toporovsky
doaj   +1 more source

A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent. [PDF]

IEEE Trans Automat Contr, 2022
Pu S, Olshevsky A, Paschalidis IC.
europepmc   +1 more source

Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction. [PDF]

Micromachines (Basel), 2023
Quan J, Yan B, Sang X, Zhong C, Li H, Qin X, Xiao R, Sun Z, Dong Y, Zhang H.   +9 more
europepmc   +1 more source