Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

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
Expand

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Solving inverse problems using data-driven models
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and… Expand
Review
2019
Review
2019
Surrogate Gradient Learning in Spiking Neural Networks
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Review
2019
Review
2019
You Only Propagate Once: Painless Adversarial Training Using Maximal Principle
Deep learning achieves state-of-the-art results in many areas. However recent works have shown that deep networks can be… Expand
  • figure 1
  • figure 2
  • table 1
  • table 2
  • table 3
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
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Review
2018
Review
2018
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Abstract Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
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
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
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
  • figure 1
  • table 1
  • table 2
  • table 3
  • table 6
Highly Cited
2005
Highly Cited
2005
Online convex optimization in the bandit setting: gradient descent without a gradient
We study a general online convex optimization problem. We have a convex set <i>S</i> and an unknown sequence of cost functions <i… Expand
Highly Cited
1999
Highly Cited
1999
Boosting Algorithms as Gradient Descent
We provide an abstract characterization of boosting algorithms as gradient decsent on cost-functionals in an inner-product… Expand
  • table 1
  • figure 1
  • figure 2
  • figure 3
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
  • figure 6
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License
ACCEPT & CONTINUE