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The State of Julia for Scientific Machine Learning

arXiv 2410.10908 ↗First posted 14 Oct 2024cs.LG cs.MS cs.PLcited by 0

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Abstract (arXiv)

Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and language-level features have grown tremendously. In this paper, we take a modern look at Julia's features and ecosystem, assess the current state of the language, and discuss its viability and pitfalls as a replacement for Python as the de-facto scientific machine learning language. We call for the community to address Julia's language-level issues that are preventing further adoption.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 97 references that are in the map. First posted on arXiv 14 Oct 2024.

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16 of 97 references.

References · 16

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4 more
  1. 2015JuMP: A Modeling Language for Mathematical Optimizationcited by 49
  2. 2020Julia Language in Machine Learning: Algorithms, Applications, and Open Issuescited by 3
  3. 2023TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Toolscited by 3
  4. 2021Applied Measure Theory for Probabilistic Modelingcited by 1

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