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Automatic Differentiation via Effects and Handlers: An Implementation in Frank

arXiv 2101.08095 ↗First posted 20 Jan 2021cs.PL cs.LGcited by 0

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Abstract (arXiv)

Automatic differentiation (AD) is an important family of algorithms which enables derivative based optimization. We show that AD can be simply implemented with effects and handlers by doing so in the Frank language. By considering how our implementation behaves in Frank's operational semantics, we show how our code performs the dynamic creation of programs during evaluation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 14 references that are in the map. First posted on arXiv 20 Jan 2021.

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2 of 14 references.

References · 2

  1. 2018Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagatorcited by 14
  2. 2016Do be do be docited by 4

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