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Gradient descent
Known as:
Descent
, Gradient descent optimization
, Gradient descent method
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Gradient descent is a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps…
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Related topics
Related topics
50 relations
AdaBoost
Backpropagation
Boltzmann machine
Conditional random field
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2019
Highly Cited
2019
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
S. Du
,
Xiyu Zhai
,
B. Póczos
,
Aarti Singh
ICLR
2019
Corpus ID: 52920808
One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can…
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Highly Cited
2018
Highly Cited
2018
The Implicit Bias of Gradient Descent on Separable Data
Daniel Soudry
,
Elad Hoffer
,
Suriya Gunasekar
,
Nathan Srebro
J. Mach. Learn. Res.
2018
Corpus ID: 3994909
We examine gradient descent on unregularized logistic regression problems, with homogeneous linear predictors on linearly…
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Review
2016
Review
2016
An overview of gradient descent optimization algorithms
Sebastian Ruder
ArXiv
2016
Corpus ID: 17485266
Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical…
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Highly Cited
2016
Highly Cited
2016
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz
,
Misha Denil
,
+4 authors
N. D. Freitas
NIPS
2016
Corpus ID: 2928017
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this…
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Highly Cited
2016
Highly Cited
2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu
,
Dilin Wang
NIPS
2016
Corpus ID: 697492
We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for…
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Highly Cited
2009
Highly Cited
2009
A coordinate gradient descent method for nonsmooth separable minimization
P. Tseng
,
S. Yun
Math. Program.
2009
Corpus ID: 28677777
We consider the problem of minimizing the sum of a smooth function and a separable convex function. This problem includes as…
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Highly Cited
2005
Highly Cited
2005
Learning to rank using gradient descent
C. Burges
,
Tal Shaked
,
+4 authors
Greg Hullender
ICML
2005
Corpus ID: 11168734
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function…
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Highly Cited
2005
Highly Cited
2005
Online convex optimization in the bandit setting: gradient descent without a gradient
A. Flaxman
,
A. Kalai
,
H. B. McMahan
SODA '05
2005
Corpus ID: 3264230
We study a general online convex optimization problem. We have a convex set <i>S</i> and an unknown sequence of cost functions <i…
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Highly Cited
1999
Highly Cited
1999
Boosting Algorithms as Gradient Descent
L. Mason
,
Jonathan Baxter
,
P. Bartlett
,
Marcus Frean
NIPS
1999
Corpus ID: 6101385
We provide an abstract characterization of boosting algorithms as gradient decsent on cost-functionals in an inner-product…
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Highly Cited
1997
Highly Cited
1997
Exponentiated Gradient Versus Gradient Descent for Linear Predictors
Jyrki Kivinen
,
Manfred K. Warmuth
Inf. Comput.
1997
Corpus ID: 6130401
We consider two algorithm for on-line prediction based on a linear model. The algorithms are the well-known Gradient Descent (GD…
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