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Found 4 papers, 2 papers with code
LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms
LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i. e., big data problems in machine learning.
Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning
Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems.
SAAGs: Biased Stochastic Variance Reduction Methods for Large-scale Learning
Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients.
Faster Learning by Reduction of Data Access Time
In this paper, we have proposed one possible solution to handle the big data problems in machine learning.
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