Results 251 to 260 of about 1,338,246 (278)

A comparative evaluation of gradient-based optimization algorithms for short-term load forecasting using deep residual networks. [PDF]

Sci Rep
Liu J, Ahmad FA, Samsudin K, Hashim F, Kadir MZAA.   +4 more
europepmc   +1 more source

Advancing Gastrointestinal Cancer Risk Prediction With Patient-Centered Machine Learning: Machine Learning Modeling Study. [PDF]

JMIR Med Inform
Baublyte D, Lee J, Gunathilake M, Kim J.
europepmc   +1 more source

A stochastic multiple gradient descent algorithm [PDF]

European Journal of Operational Research, 2018
International audienceIn this article, we propose a new method for multiobjective optimization problems in which the objective functions are expressed as expectations of random functions.
Fabrice Poirion, Quentin Mercier, Jean-Antoine Desideri   +2 more
exaly   +6 more sources

Stochastic Gradient Descent on Riemannian Manifolds [PDF]

IEEE Transactions on Automatic Control, 2013
Stochastic gradient descent is a simple approach to find the local minima of a cost function whose evaluations are corrupted by noise. In this paper, we develop a procedure extending stochastic gradient descent algorithms to the case where the function is defined on a Riemannian manifold.
Silvere Bonnabel
exaly   +4 more sources

Stochastic Gradient Descent in Continuous Time [PDF]

SIAM Journal on Financial Mathematics, 2017
Stochastic gradient descent in continuous time (SGDCT) provides a computationally efficient method for the statistical learning of continuous-time models, which are widely used in science, engineering, and finance. The SGDCT algorithm follows a (noisy) descent direction along a continuous stream of data.
Konstantinos Spiliopoulos
exaly   +4 more sources

Backpropagation and stochastic gradient descent method

Neurocomputing, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shun-Ichi Amari
exaly   +5 more sources

Some of the next articles are maybe not open access.

Related searches:

Stochastic gradient descent possibilistic clustering

11th Hellenic Conference on Artificial Intelligence, 2020
Although online versions of several well known clustering algorithms have been proposed, in order to deal effectively with the big data issue, as well as with the case where the data are available in a streaming fashion, very few of them follow the stochastic gradient descent philosophy.
Aggeliki Koutsibella, Konstantinos D. Koutroumbas   +1 more
openaire   +2 more sources

Stochastic Gradient Descent with GPGPU

2012
We show how to optimize a Support Vector Machine and a predictor for Collaborative Filtering with Stochastic Gradient Descent on the GPU, achieving 1.66 to 6-times accelerations compared to a CPU-based implementation. The reference implementations are the Support Vector Machine by Bottou and the BRISMF predictor from the Netflix Prices winning team ...
David Zastrau, Stefan Edelkamp
openaire   +1 more source

Runtime Analysis of Stochastic Gradient Descent

Proceedings of the 4th International Conference on Computer Science and Application Engineering, 2020
Stochastic gradient descent (SGD) is one of the most famous methods for minimization. However, there are few results about the runtime analysis of SGD because of its randomness. In this paper, we explain how to approximate SGD by stochastic differential equations (SDE).
Guanqiang Hu, Yushan Zhang
openaire   +1 more source

A note on diffusion limits for stochastic gradient descent [PDF]

Journal of Approximation Theory
In the machine learning literature stochastic gradient descent has recently been widely discussed for its purported implicit regularization properties. Much of the theory, that attempts to clarify the role of noise in stochastic gradient algorithms, has ...
Alberto Lanconelli
exaly   +2 more sources