publication . Preprint . 2017

Owl: A General-Purpose Numerical Library in OCaml

Wang, Liang;
Open Access English
  • Published: 30 Jul 2017
Abstract
Owl is a new numerical library developed in the OCaml language. It focuses on providing a comprehensive set of high-level numerical functions so that developers can quickly build up data analytical applications. In this abstract, we will present Owl's design, core components, and its key functionality.
Subjects
free text keywords: Computer Science - Mathematical Software, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Logic in Computer Science
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[1] Julia language. https://julialang.org/, No date. Accessed January 20, 2017.

[2] Numpy. https://github.com/numpy/numpy, No date. Accessed January 20, 2017.

[3] Abadi et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.

[4] A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind. Automatic differentiation in machine learning: a survey. arXiv preprint arXiv:1502.05767, 2015. [OpenAIRE]

[5] A. Griewank and A. Walther. Evaluating derivatives: principles and techniques of algorithmic differentiation. SIAM, 2008. [OpenAIRE]

[6] E. Jones, T. Oliphant, and P. Peterson. {SciPy}: open source scientific tools for {Python}. 2014.

Abstract
Owl is a new numerical library developed in the OCaml language. It focuses on providing a comprehensive set of high-level numerical functions so that developers can quickly build up data analytical applications. In this abstract, we will present Owl's design, core components, and its key functionality.
Subjects
free text keywords: Computer Science - Mathematical Software, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Logic in Computer Science
Download from

[1] Julia language. https://julialang.org/, No date. Accessed January 20, 2017.

[2] Numpy. https://github.com/numpy/numpy, No date. Accessed January 20, 2017.

[3] Abadi et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.

[4] A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind. Automatic differentiation in machine learning: a survey. arXiv preprint arXiv:1502.05767, 2015. [OpenAIRE]

[5] A. Griewank and A. Walther. Evaluating derivatives: principles and techniques of algorithmic differentiation. SIAM, 2008. [OpenAIRE]

[6] E. Jones, T. Oliphant, and P. Peterson. {SciPy}: open source scientific tools for {Python}. 2014.

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