Results 51 to 60 of about 22,588 (154)
Distributed stochastic gradient descent for link prediction in signed social networks
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
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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Graph Drawing by Stochastic Gradient Descent [PDF]
Submitted to IEEE Transactions on Visualization and Computer Graphics on 26/06 ...
Jonathan X. Zheng +2 more
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Equating quantum imaginary time evolution, Riemannian gradient flows, and stochastic implementations
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
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Distributed Stochastic Gradient Descent With Compressed and Skipped Communication
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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Text Sentiment Analysis Based on Hybrid Chi-square Statistic and Logistic Regression [PDF]
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
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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Stochastic Adaptive Gradient Descent Without Descent
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
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On Scalable Inference with Stochastic Gradient Descent
In many applications involving large dataset or online updating, stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates and has gained increasing popularity due to its numerical convenience and memory efficiency. While the asymptotic properties of SGD-based estimators have been established decades ago, statistical ...
Yixin Fang, Jinfeng Xu, Lei Yang
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Attentional-Biased Stochastic Gradient Descent
In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch.
Qi Qi 0006 +4 more
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