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Gradient descent

Known as: Descent, Gradient descent optimization, Gradient descent method 
Gradient descent is a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps… Expand
Wikipedia

Related topics

Related topics

50 relations
AdaBoostBackpropagationBoltzmann machineConditional random field
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix… Expand
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Review
2019
Review
2019
A Survey on Network Embedding
Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure… Expand
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Review
2018
Review
2018
Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey
In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination… Expand
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Review
2018
Review
2018
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation: Recent Theory and Fast Algorithms via Convex and Nonconvex Optimization
Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative… Expand
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Review
2017
Review
2017
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources
The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user… Expand
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Review
2016
Review
2016
Deep Learning in Natural Language Processing
Deep learning has emerged as a highly efficient technique in machine learning to perform text analytics, which includes text… Expand
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Review
2016
Review
2016
An overview of gradient descent optimization algorithms
Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical… Expand
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Highly Cited
2016
Highly Cited
2016
Learning to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this… Expand
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Highly Cited
2005
Highly Cited
2005
Learning to rank using gradient descent
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function… Expand
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Highly Cited
1997
Highly Cited
1997
Exponentiated Gradient Versus Gradient Descent for Linear Predictors
We consider two algorithm for on-line prediction based on a linear model. The algorithms are the well-known Gradient Descent (GD… Expand
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