Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
Machine Learning
2018-07-18 v1 Optimization and Control
Machine Learning
Abstract
This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or loss) functions, convex optimization algorithms (including Gradient Descent, L-BFGS, Stochastic Gradient Descent, Conjugate Gradient, etc.), and a family of machine learning classifiers and regressors (Logistic Regression, SVMs, Least Square Regression, etc.). This framework makes it possible to deploy and train models with a few lines of code, and also extend and build upon this by integrating new loss functions and optimization algorithms.
Cite
@article{arxiv.1807.06574,
title = {Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization},
author = {Rishabh Iyer and John T. Halloran and Kai Wei},
journal= {arXiv preprint arXiv:1807.06574},
year = {2018}
}