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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… 
2014
2014
Development of a stochastic simulation–optimization model for planning electric power systems – A case study of Shanghai, China
2014
2014
Smoothed Gradients for Stochastic Variational Inference
Stochastic variational inference (SVI) lets us scale up Bayesian computation to massive data. It uses stochastic optimization to… 
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… 
2005
2005
Stochastic texture analysis for monitoring stochastic processes in industry
Highly Cited
2003
Highly Cited
2003
A Bayesian approach to the ecosystem inverse problem
1998
1998
A Randomized Algorithm for Pairwise Clustering
We present a stochastic clustering algorithm based on pairwise similarity of datapoints. Our method extends existing… 
1992
1992
Prototype-based discriminative training for various speech units
It has since been shown that learning vector quantisation (LVQ) is a special case of a more general method, generalized… 
Highly Cited
1990
Highly Cited
1990
Efficient search procedures for selecting the optimum innovation in stochastic coders
The authors describe several procedures that simplify the search in stochastic coders, but do not put constraints on the… 
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