Results 251 to 260 of about 1,338,397 (280)

A comparative analysis of machine learning classifiers for modeling the number of liveborn piglets. [PDF]

J Anim Sci
Yang J, Jafarikia M, Gagnon P, Maignel L, DeVries B, Li J, Tulpan D.   +6 more
europepmc   +1 more source

A Resilient Cloud-Edge Digital Twin Framework for Urban UAV Logistics Under 3D Blockages and ADS-B Signal Anomalies. [PDF]

Sensors (Basel)
Tong H, Chen Y, Liu Y, Huang F, Sun J.
europepmc   +1 more source

Correction to "Pneumonia Detection in Veterinary X-Rays Utilizing PaddleSeg-Based Semantic Segmentation and Mamba-Powered Classification". [PDF]

Vet Radiol Ultrasound
europepmc   +1 more source

Enhanced Line Search Improves Robustness and Efficiency of Pose Sampling in Protein-Ligand Docking. [PDF]

J Chem Theory Comput
Gaskin L, Welsch M, Kirchmair J, Kimiaei M.   +3 more
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

Preconditioned Stochastic Gradient Descent [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2018
13 pages, 9 figures. To appear in IEEE Transactions on Neural Networks and Learning Systems.
Xi-Lin Li
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

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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