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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… 
2016
2016
Improving Stochastic Gradient Descent with Feedback
In this paper we propose a simple and efficient method for improving stochastic gradient descent methods by using feedback from… 
2015
2015
Stochastic gradient descent on GPUs
Irregular algorithms such as Stochastic Gradient Descent (SGD) can benefit from the massive parallelism available on GPUs… 
Highly Cited
2007
Highly Cited
2007
Utilizing Variational Optimization to Learn Markov Random Fields
  • M. Tappen
  • IEEE Conference on Computer Vision and Pattern…
  • 2007
  • Corpus ID: 2468599
Markov random field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for… 
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… 
2005
2005
Natural Convection in a Closed Cavity under Stochastic Non-Boussinesq Conditions
A stochastic projection method (SPM) is developed for quantitative propagation of uncertainty in compressible zero-Mach-number… 
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… 
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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