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Stochastic gradient descent

Known as: Gradient descent in machine learning, SGD (disambiguation), AdaGrad 
Stochastic gradient descent (often shortened in SGD), also known as incremental gradient descent, is a stochastic approximation of the gradient… 
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Related topics

Related topics

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Ant colony optimization algorithmsApache SparkArtificial neural networkBackpropagation

Broader (1)

Stochastic optimization

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Stochastic Gradient Descent with Differentially Private Updates
In recent decades, the amount of data available has grown immensely. A lot of this data may be private or sensitive. Privacy of… 
2015
2015
Stochastic gradient descent on GPUs
Irregular algorithms such as Stochastic Gradient Descent (SGD) can benefit from the massive parallelism available on GPUs… 
2012
2012
Stochastic Gradient Descent with GPGPU
We show how to optimize a Support Vector Machine and a predictor for Collaborative Filtering with Stochastic Gradient Descent on… 
Highly Cited
2007
Highly Cited
2007
Solving transportation bi-level programs with Differential Evolution
Bi-level programming problems arise in situations when the decision maker has to take into account the responses of the users to… 
Highly Cited
2006
Highly Cited
2006
Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours
Based on recent work on Stochastic Partial Differential Equations (SPDEs), this paper presents a simple and well-founded method… 
2003
2003
Seismic pore pressure prediction with uncertainty using a probabilistic mechanical earth model
Summary We propose a methodology to propagate uncertainties in seismic pore pressure prediction using a 3-D Probabilistic… 
Highly Cited
2001
Highly Cited
2001
Polarization diversity in MIMO radio channels: experimental validation of a stochastic model and performance assessment
A stochastic MIMO radio channel considering (i) polarization diversity and (ii) unbalanced branch power ratio (BPR) is being… 
1998
1998
A Randomized Algorithm for Pairwise Clustering
We present a stochastic clustering algorithm based on pairwise similarity of datapoints. Our method extends existing… 
Highly Cited
1993
Highly Cited
1993
Reduction of coding artifacts in transform image coding
The problem of image decompression is cast as an ill-posed inverse problem, and a stochastic regularization technique is used to… 
Highly Cited
1965
Highly Cited
1965
Orbits of ¹-functions under doubly stochastic transformations
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