Gradient descent

Known as: Gradient descent optimization, Gradient descent method, Steepest descent 
Gradient descent is a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps… (More)
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Topic mentions per year

Topic mentions per year

1937-2018
05001000150019372017

Papers overview

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Highly Cited
2013
Highly Cited
2013
Stochastic gradient descent is popular for large scale optimization but has slow convergence asymptotically due to the inherent… (More)
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Highly Cited
2012
Highly Cited
2012
Chapter 1 strongly advocates the stochastic back-propagation method to train neural networks. This is in fact an instance of a… (More)
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Highly Cited
2011
Highly Cited
2011
Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve stateof-the-art performance on a variety of machine… (More)
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Highly Cited
2011
Highly Cited
2011
We provide a novel algorithm to approximately factor large matrices with millions of rows, millions of columns, and billions of… (More)
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Highly Cited
2010
Highly Cited
2010
<lb>With the increase in available data parallel machine learning has become an in-<lb>creasingly pressing problem. In this paper… (More)
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Highly Cited
2007
Highly Cited
2007
We describe and analyze a simple and effective iterative algorithm for solving the optimization problem cast by Support Vector… (More)
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Highly Cited
2005
Highly Cited
2005
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function… (More)
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Highly Cited
1999
Highly Cited
1999
Much recent attention, both experimental and theoretical, has been focussed on classication algorithms which produce voted… (More)
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Highly Cited
1997
Highly Cited
1997
We consider two algorithms for on-line prediction based on a linear model. The algorithms are the well-known gradient descent (GD… (More)
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Highly Cited
1994
Highly Cited
1994
Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or… (More)
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