Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization
This toolkit addresses the need for scalable and extensible software in machine learning and optimization, but it is incremental as it builds on existing methods.
The authors introduced Jensen, a C++ toolkit for production-level machine learning and convex optimization, enabling deployment and training of models with minimal code and extensibility for new functions and algorithms.
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.