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

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, Conjuga...

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Bibliographic Details
Published inarXiv.org
Main Authors Iyer, Rishabh, Halloran, John T, Wei, Kai
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.07.2018
Subjects
Algorithms
Artificial intelligence
Computational geometry
Convex analysis
Convexity
Extensibility
Machine learning
Optimization
Optimization algorithms
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Summary: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.
ISSN:2331-8422