Distributed and asynchronous Stochastic Gradient Descent with variance reduction

Ming, Yuewei; Zhao, Yawei; Wu, Chengkun; Li, Kuan; Yin, Jianping

doi:10.1016/j.neucom.2017.11.044

Cited by 20 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RS-DCNN approach primarily implements two steps: (1) preparation of the training dataset which is done by dividing large RS satellite images into smaller images and then, implementing an algorithm called Maximum Likelihood which is a supervised classification algorithm (2) A distributed CNN algorithm is applied to carry out accurate classification of the big satellite images. For bringing in parallelism for image classification, the Asynchronous Distributed Stochastic Gradient Descent (ADSGD) algorithm [24] is used which distributes the execution of the CNN algorithm across the entire big data cluster.…”

Section: Rs-dcnn: Distributed Cnn To Classify Remote Sensing Imagesmentioning

confidence: 99%

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Murthy M S

2023

IJEER

View full text Add to dashboard Cite

Remotely sensed images, their classification and accuracy play a vital role in measuring a country’s scientific growth and technological development. Remote Sensing (RS) can be interpreted as a way of assessing the characteristics of a surface or an entity from a distance. This task of identifying and classifying datasets of RS images can be done using Convolutional Neural Network (CNN). For classifying images of large-scale areas, the traditional CNN approach produces coarse maps. For addressing this issue, Object based CNN method can be used. Classifying images with high spatial resolution can be done effectively using Object based image analysis. Deep learning methods offer the strength of auto learning the spatial features of an image. Object scale based adaptive CNN is a novel technique that can improve the accuracy of image classification of high spatial resolution images. For efficient RS image classification, a novel Deep learning approach called distributed CNN can be used which leads to enhanced accuracy of RS image classification. In this paper, three CNN models have been compared while considering the training time and efficiency to classify RS images as parameters of measure to assess the CNN models.

show abstract

Section: Rs-dcnn: Distributed Cnn To Classify Remote Sensing Imagesmentioning

confidence: 99%

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Murthy M S

2023

IJEER

View full text Add to dashboard Cite

show abstract

“…The determination of confidence regions and a stopping criterion are based on information from iteration and advantage in priori rules. The stochastic approximation method is an iteration method that uses the function of estimates to find the function root [39][40][41][42][43].…”

Section: Projected Stochastic Gradient (Psg)mentioning

confidence: 99%

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Alkahtani

2022

Sustainability

View full text Add to dashboard Cite

Supply chain management (SCM) is considered at the forefront of many organizations in the delivery of their products. Various optimization methods are applied in the SCM to improve the efficiency of the process. In this research, the projected stochastic gradient (PSG) method was proposed to increase the efficiency of the SCM analysis. The key objective of an efficient supply chain is to find the best flow patterns for the best products in order to select the suppliers to different customers. Hence, the focus of this research is on developing an efficient multi-echelon supply chain using factors such as cost, time, and risk. In the convex case, the proposed method has the advantage of a weakly convergent sequence of iterates to a point in the set of minimizers with probability one. The developed method achieves strong sequence convergence to the unique optimum, with probability one. The SCM dataset was utilized to assess the proposed method’s performance. The proposed PSG method has the advantage of considering the holding cost in the profit analysis of the company. The results of the developed PSG method are analyzed according to the product’s profit, stock, and demand. The proposed PSG method also provides the prediction of demand to increase profit.

show abstract

“…The SGD algorithm has been demonstrated to be very useful in training a variety of DNNs. Some related works have been proposed to speed up the efficiency of the SGD training through the parallel and distributed training [15,16]. Hogwild!…”

Section: Related Workmentioning

confidence: 99%

Developing a Loss Prediction-based Asynchronous Stochastic Gradient Descent Algorithm for Distributed Training of Deep Neural Networks

Ren

et al. 2020

49th International Conference on Parallel Processing - ICPP

View full text Add to dashboard Cite

show abstract

Distributed and asynchronous Stochastic Gradient Descent with variance reduction

Cited by 20 publications

References 6 publications

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

A Comparative Study of the CNN Based Models Used for Remote Sensing Image Classification

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Developing a Loss Prediction-based Asynchronous Stochastic Gradient Descent Algorithm for Distributed Training of Deep Neural Networks

Contact Info

Product

Resources

About