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arXiv · 1807.06574

Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization

Rishabh Iyer·John T. Halloran·Kai Wei
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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.

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Rishabh Iyer, John T. Halloran, Kai Wei. 2018-07-17. Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization. https://arxiv.org/abs/1807.06574

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