Results 51 to 60 of about 142,362 (315)
Stochastic Gradient Descent on Riemannian Manifolds [PDF]
IEEE Transactions on Automatic Control, 2013 Stochastic gradient descent is a simple approach to find the local minima of a cost function whose evaluations are corrupted by noise. In this paper, we develop a procedure extending stochastic gradient descent algorithms to the case where the function is defined on a Riemannian manifold.
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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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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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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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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Peripheral neuropathies contribute to patient disability but may be diagnosed late or missed altogether due to late referral, limitation of current diagnostic methods and lack of specialized testing facilities. To address this clinical gap, we developed NeuropathAI, an interpretable deep learning–based multiclass classification ...
Chaima Ben Rabah +7 more
wiley +1 more source
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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The effective noise of stochastic gradient descent
Journal of Statistical Mechanics: Theory and Experiment, 2022 Abstract Stochastic gradient descent (SGD) is the workhorse algorithm of deep learning technology. At each step of the training phase, a mini batch of samples is drawn from the training dataset and the weights of the neural network are adjusted according to the performance on this specific subset of examples.
Mignacco, Francesca +1 more
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Advanced 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
Advanced Engineering Materials, EarlyView.In this experimental study, the mechanical properties of additively manufactured Ti‐6Al‐4V lattice structures of different geometries are characterized using compression, four point bending and fatigue testing. While TPMS designs show superior fatigue resistance, SplitP and Honeycomb lattice structures combine high stiffness and strength. The resulting
Klaus Burkart +3 more
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