Distributed stochastic gradient descent for link prediction in signed social networks
EURASIP Journal on Advances in Signal Processing, 2019 This paper considers the link prediction problem defined over a signed social network, where the relationship between any two network users can be either positive (friends) or negative (foes).
Han Zhang, Gang Wu, Qing Ling
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Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework
Tongxin 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
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On the Generalization of Stochastic Gradient Descent with Momentum
J. Mach. Learn. Res., 2018 While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that there exists a convex loss function for which the stability gap for multiple epochs of SGD with ...
Ramezani-Kebrya, Ali +4 more
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Equating quantum imaginary time evolution, Riemannian gradient flows, and stochastic implementations
Physical Review ResearchWe 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
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Distributed Stochastic Gradient Descent With Compressed and Skipped Communication
IEEE 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
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Stochastic Adaptive Gradient Descent Without Descent
CoRRWe 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
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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 +2 more
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Stabilizing updates in differentially private stochastic gradient descent with buffered rejection
Scientific ReportsDifferentially private stochastic gradient descent is a standard algorithm for training deep models on sensitive data, but under tight privacy budgets it must add large noise to every step, which slows convergence and reduces accuracy.
Sifan Deng +4 more
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Text Sentiment Analysis Based on Hybrid Chi-square Statistic and Logistic Regression [PDF]
Jisuanji 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
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Fractional Stochastic Search Algorithms: Modelling Complex Systems via AI
Mathematics, 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
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