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The development of the mlpack C++ machine learning library (http://www.mlpack.org/) has required the design and implementation of a flexible, robust optimization system that is able to solve the types of arbitrary optimization problems that may arise all throughout machine learning problems.
The regression analysis of binary sequences
David R Cox · 1958
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Jacob Goldberger, Geoffrey E Hinton, Sam T Roweis, and Ruslan R Salakhutdinov · 2005
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MLPACK: A scalable C++ machine learning library
Ryan R. Curtin, James R. Cline, Neil P. Slagle, William B. March, P. Ram, Nishant A. Mehta, and Alexander G. Gray · 2013
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
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Eric Jones, Travis Oliphant, and Pearu Peterson · 2014
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Jürgen Schmidhuber · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Later among the works it cites.
Designing and building the mlpack open-source machine learning library
Ryan R. Curtin and Marcus Edel · 2017
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Matlab optimization toolbox, 2017
Mathworks · 2017
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Armadillo: C++ template metaprogramming for compile-time optimization of linear algebra
Conrad Sanderson and Ryan R. Curtin · 2017
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