Results 51 to 60 of about 22,732 (252)

Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework

open access: yesTongxin xuebao, 2018
Aiming at the contradiction between the efficiency and privacy of stochastic gradient descent algorithm in distributed computing environment,a stochastic gradient descent algorithm preserving differential privacy based on MapReduce was proposed.Based on ...
Yihan YU, Yu FU, Xiaoping WU
doaj   +2 more sources

Graph Drawing by Stochastic Gradient Descent [PDF]

open access: yesIEEE Transactions on Visualization and Computer Graphics, 2019
Submitted to IEEE Transactions on Visualization and Computer Graphics on 26/06 ...
Jonathan X. Zheng   +2 more
openaire   +4 more sources

PAK1 activation drives divergent resistance mechanisms to aromatase inhibition and tamoxifen in a luminal: A breast cancer model

open access: yesMolecular Oncology, EarlyView.
Breast cancer remains a major cause of cancer death in women, frequently developing endocrine therapy resistance. This study demonstrates that upregulated p21‐activated kinase 1 (PAK1) activity drives resistance to tamoxifen and long‐term estrogen deprivation in ER+ breast cancer models.
Luisa Schwarzmüller   +10 more
wiley   +1 more source

Observer‐Based Adaptive Event‐Triggered Tracking Control for Fuzzy TS Systems With Premise Mismatch

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This paper presents an adaptive logistic event‐triggered observer‐based tracking controller for Takagi‐Sugeno fuzzy systems under constrained inputs and network delays. Leveraging a hybrid LMI and Secretary Bird Optimization approach, this strategy significantly minimizes communication overhead and computational burden while ensuring optimal reference ...
Oussama Djadane   +3 more
wiley   +1 more source

Equating quantum imaginary time evolution, Riemannian gradient flows, and stochastic implementations

open access: yesPhysical Review Research
We identify quantum imaginary time evolution as a Riemannian gradient flow on the unitary group. We develop an upper bound for the error between the two evolutions that can be controlled through the step size of the Riemannian gradient descent that ...
Nathan A. McMahon   +2 more
doaj   +1 more source

Distributed Stochastic Gradient Descent With Compressed and Skipped Communication

open access: yesIEEE Access, 2023
This paper introduces CompSkipDSGD, a new algorithm for distributed stochastic gradient descent that aims to improve communication efficiency by compressing and selectively skipping communication.
Tran Thi Phuong   +2 more
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

open access: yesAdvanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt   +8 more
wiley   +1 more source

Text Sentiment Analysis Based on Hybrid Chi-square Statistic and Logistic Regression [PDF]

open access: yesJisuanji gongcheng, 2017
In text sentiment analysis,feature extraction method based on Chi-square statistic (CHI) is easy to ignore single text word frequency which leads to text feature accuary is low,a feature extraction method based on hybrid chi-square statistics is proposed.
LI Ping,DAI Yueming,WANG Yan
doaj   +1 more source

Fractional Stochastic Search Algorithms: Modelling Complex Systems via AI

open access: yesMathematics, 2023
The aim of this article is to establish a stochastic search algorithm for neural networks based on the fractional stochastic processes {BtH,t≥0} with the Hurst parameter H∈(0,1).
Bodo Herzog
doaj   +1 more source

Stochastic Adaptive Gradient Descent Without Descent

open access: yesCoRR
We introduce a new adaptive step-size strategy for convex optimization with stochastic gradient that exploits the local geometry of the objective function only by means of a first-order stochastic oracle and without any hyper-parameter tuning. The method comes from a theoretically-grounded adaptation of the Adaptive Gradient Descent Without Descent ...
Jean-François Aujol   +2 more
openaire   +2 more sources

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