stochasticad.jl

Research package for automatic differentiation of programs containing discrete randomness.

https://github.com/gaurav-arya/stochasticad.jl

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (8.8%) to scientific vocabulary

Keywords from Contributors

interpretability surrogate multilayer-networks hack standardization pdes probabilistic-programming julia-package ocean fluid-dynamics
Last synced: 6 months ago · JSON representation ·

Repository

Research package for automatic differentiation of programs containing discrete randomness.

Basic Info
  • Host: GitHub
  • Owner: gaurav-arya
  • License: mit
  • Language: Julia
  • Default Branch: main
  • Homepage:
  • Size: 47.3 MB
Statistics
  • Stars: 207
  • Watchers: 7
  • Forks: 15
  • Open Issues: 24
  • Releases: 27
Created over 3 years ago · Last pushed 11 months ago
Metadata Files
Readme License Citation

README.md

StochasticAD

Build Status arXiv article

StochasticAD is an experimental, research package for automatic differentiation (AD) of stochastic programs. It implements AD algorithms for handling programs that can contain discrete randomness, based on the methodology developed in this NeurIPS 2022 paper. We're still working on docs and code cleanup!

Installation

The package can be installed with the Julia package manager:

julia julia> using Pkg; julia> Pkg.add("StochasticAD");

Citation

@inproceedings{arya2022automatic, author = {Arya, Gaurav and Schauer, Moritz and Sch\"{a}fer, Frank and Rackauckas, Christopher}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {10435--10447}, publisher = {Curran Associates, Inc.}, title = {Automatic Differentiation of Programs with Discrete Randomness}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/43d8e5fc816c692f342493331d5e98fc-Paper-Conference.pdf}, volume = {35}, year = {2022} }

Owner

  • Name: Gaurav Arya
  • Login: gaurav-arya
  • Kind: user
  • Location: Cambridge, MA
  • Company: MIT

Citation (CITATION.bib)

@inproceedings{arya2022automatic,
 author = {Arya, Gaurav and Schauer, Moritz and Sch\"{a}fer, Frank and Rackauckas, Christopher},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {10435--10447},
 publisher = {Curran Associates, Inc.},
 title = {Automatic Differentiation of Programs with Discrete Randomness},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/43d8e5fc816c692f342493331d5e98fc-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

GitHub Events

Total
  • Issues event: 5
  • Watch event: 10
  • Issue comment event: 5
  • Push event: 2
  • Pull request event: 2
  • Fork event: 1
  • Create event: 2
Last Year
  • Issues event: 5
  • Watch event: 10
  • Issue comment event: 5
  • Push event: 2
  • Pull request event: 2
  • Fork event: 1
  • Create event: 2

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 319
  • Total Committers: 8
  • Avg Commits per committer: 39.875
  • Development Distribution Score (DDS): 0.075
Past Year
  • Commits: 76
  • Committers: 1
  • Avg Commits per committer: 76.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Gaurav Arya g****2@g****m 295
Moritz Schauer m****r@w****e 18
Frank Schaefer k****e@w****e 1
Guillaume Dalle 2****e 1
Moelf p****n@j****v 1
Pietro Monticone 3****e 1
Sean 1****9 1
github-actions[bot] 4****] 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 30
  • Total pull requests: 109
  • Average time to close issues: 27 days
  • Average time to close pull requests: 2 days
  • Total issue authors: 13
  • Total pull request authors: 10
  • Average comments per issue: 3.03
  • Average comments per pull request: 0.56
  • Merged pull requests: 96
  • Bot issues: 0
  • Bot pull requests: 6
Past Year
  • Issues: 6
  • Pull requests: 12
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 12 minutes
  • Issue authors: 4
  • Pull request authors: 4
  • Average comments per issue: 3.5
  • Average comments per pull request: 0.5
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 4
Top Authors
Issue Authors
  • gaurav-arya (8)
  • arnauqb (3)
  • AndresCenteno (3)
  • GuusAvis (3)
  • AlCap23 (2)
  • slwu89 (2)
  • frankschae (2)
  • ChrisRackauckas (1)
  • FHoltorf (1)
  • Eemilp (1)
  • mschauer (1)
  • lhuangb (1)
  • JuliaTagBot (1)
Pull Request Authors
  • gaurav-arya (88)
  • mschauer (12)
  • github-actions[bot] (11)
  • GuusAvis (4)
  • Moelf (2)
  • Krastanov (2)
  • slwu89 (1)
  • pitmonticone (1)
  • tlow22 (1)
  • frankschae (1)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

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