ad-delcont: Reverse-mode automatic differentiation with delimited continuations
Reverse-mode automatic differentiation using delimited continuations (shift/reset), inspired by the papers
F. Wang et al, Backpropagation with Continuation Callbacks : Foundations for Efficient and Expressive Differentiable Programming, NeurIPS 2018 - https://papers.nips.cc/paper/2018/file/34e157766f31db3d2099831d348a7933-Paper.pdf
F. Wang et al, Demystifying Differentiable Programming : Shift/Reset the Penultimate Backpropagator, ICFP 2019 - https://www.cs.purdue.edu/homes/rompf/papers/wang-icfp19.pdf
The package exposes a small and easily extensible user interface to automatic differentiation combinators. It also manages to be very lightweight, as it only depends on base and transformers.
To use the library, import Numeric.AD.DelCont, which also contains all documentation.
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- ad-delcont-0.1.0.0.tar.gz [browse] (Cabal source package)
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| Versions [RSS] | 0.1.0.0, 0.2.0.0, 0.3.0.0, 0.5.0.0 |
|---|---|
| Change log | Changelog.md |
| Dependencies | base (>=4.7 && <5), transformers (>=0.5) [details] |
| License | BSD-3-Clause |
| Copyright | (c) 2021 Marco Zocca |
| Author | Marco Zocca |
| Maintainer | ocramz |
| Category | Math, Numeric, Machine Learning, Optimization, Optimisation |
| Home page | https://github.com/ocramz/ad-delcont |
| Source repo | head: git clone https://github.com/ocramz/ad-delcont |
| Uploaded | by ocramz at 2021-07-18T16:21:35Z |
| Distributions | LTSHaskell:0.5.0.0, NixOS:0.5.0.0, Stackage:0.5.0.0 |
| Downloads | 632 total (23 in the last 30 days) |
| Rating | 2.25 (votes: 2) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2021-07-18 [all 1 reports] |