Tangent: Automatic Differentiation Using Source Code Transformation in Python
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Abstract (arXiv)
Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT) in Python. It takes numeric functions written in a syntactic subset of Python and NumPy as input, and generates new Python functions which calculate a derivative. This approach to automatic differentiation is different from existing packages popular in machine learning, such as TensorFlow and Autograd. Advantages are that Tangent generates gradient code in Python which is readable by the user, easy to understand and debug, and has no runtime overhead. Tangent also introduces abstractions for easily injecting logic into the generated gradient code, further improving usability.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Nov 2017.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2021gradSim: Differentiable simulation for system identification and visuomotor controlcited by 100
- 2023Generalized Neural Closure Models with Interpretabilitycited by 8
- 2021Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetrycited by 3
- 2025Hybrid twinning using PBDW and DeepONet for the effective state estimation and prediction on partially known systemscited by 0
- 2025Hybrid twinning using PBDW and DeepONet for the effective state estimation and prediction on partially known systems3 Dec 2025
- 2023Generalized Neural Closure Models with Interpretability15 Jan 2023
- 2021Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry23 Nov 2021
- 2021gradSim: Differentiable simulation for system identification and visuomotor control6 Apr 2021