mlpack 4: a fast, header-only C++ machine learning library

Abstract

For over 15 years, the mlpack machine learning library has served as a "swiss army knife'' for C++-based machine learning (Curtin et al., 2013). Its efficient implementations of common and cutting-edge machine learning algorithms have been used in a wide variety of scientific and industrial applications. This paper overviews mlpack 4, a significant upgrade over its predecessor (Curtin et al., 2018). The library has been significantly refactored and redesigned to facilitate an easier prototyping-to-deployment pipeline, including bindings to other languages (Python, Julia, R, Go, and the command line) that allow prototyping to be seamlessly performed in environments other than C++.

Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

“…4). One of the drivers of recent adoption-growth is the release of accessible AI tools and platforms which have emerged over the past several years [1], [11], [49], [60], such as scikit-learn, TensorFlow, Theano, Caffe, Keras, MXNet, mlpack, PyTorch, CNTK, Auto ML, Open NN and H2O. These and other such tools made AI much more readily available to scientists and researchers from diverse disciplinary backgrounds.…”

StarFunc: fusing template-based and deep learning approaches for accurate protein function prediction

“…Here, 𝑤 Q and 𝑤 0 are parameters of a GO term-specific logistic regression model trained by mlpack (25) using options "--max_iterations 100 --tolerance 1e-4 --lambda 0.5". 𝑥 0 = 1 − 𝐸 0 /1000 indicates the match of the query sequence to the k-th Pfam family, where 𝐸 0 is the E-value from HMMsearch.…”

A simple point cloud file format and open-source implementation for geospatial analysis and software development

“…Machine learning features are commonly associated with individual points such that tabular data can be formed in which rows correspond to samples and columns correspond to features. This provides a convenient way to generate matrices that can be directly input into, for example, Scikit Learn 11 , PyTorch/LibTorch 12 , ML_pack 13 , DLib 14 , …, etc.…”