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FoRECAST develops advanced programming tools for automatic differentiation and probabilistic programming, enhancing calculations involving derivatives and probabilities. The project aims to create a user-friendly system that supports complex modeling tasks across various scientific domains, including machine learning a…
Automatic Differentiation (AD) systems, like TensorFlow, and probabilistic programming languages (PPLs), like Stan, automate complex computations of derivatives and Bayesian inference tasks.
By stream- lining these computations for non-expert users, these high-level systems have accelerated progress across science and society (e.g. by enabling machine learning).
Yet, the theoretical foundations needed to build a high-level system for composable programming with derivatives and probabilities are…
UNIVERSITEIT UTRECHT
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