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Computer Science > Machine Learning

arXiv:2403.14606 (cs)
[Submitted on 21 Mar 2024]

Title:The Elements of Differentiable Programming

Authors:Mathieu Blondel, Vincent Roulet
Download a PDF of the paper titled The Elements of Differentiable
Programming, by Mathieu Blondel and 1 other authors
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    Abstract:Artificial intelligence has recently experienced
    remarkable advances, fueled by large models, vast datasets,
    accelerated hardware, and, last but not least, the transformative
    power of differentiable programming. This new programming
    paradigm enables end-to-end differentiation of complex computer
    programs (including those with control flows and data
    structures), making gradient-based optimization of program
    parameters possible. As an emerging paradigm, differentiable
    programming builds upon several areas of computer science and
    applied mathematics, including automatic differentiation,
    graphical models, optimization and statistics. This book presents
    a comprehensive review of the fundamental concepts useful for
    differentiable programming. We adopt two main perspectives, that
    of optimization and that of probability, with clear analogies
    between the two. Differentiable programming is not merely the
    differentiation of programs, but also the thoughtful design of
    programs intended for differentiation. By making programs
    differentiable, we inherently introduce probability distributions
    over their execution, providing a means to quantify the
    uncertainty associated with program outputs.

Comments: Draft version 1
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
          Programming Languages (cs.PL)
Cite as:  arXiv:2403.14606 [cs.LG]
          (or arXiv:2403.14606v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2403.14606
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Mathieu Blondel [view email]
[v1] Thu, 21 Mar 2024 17:55:16 UTC (1,921 KB)
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