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Probabilistic Circuits

This repo contains the source code for the website https://arranger1044.github.io/probabilistic-circuits/ which is a curated and reasoned list of papers on probabilistic circuits (PCs), computational graphs encoding tractable probability distributions.

License

CC0

All the material in this repo is released to the Public Domain. Feel free to clone, fork or complete and/or correct any of these lists.

How to contribute

To add, change or remove a paper on the website, please open a pull request!

This site harness Jekyll templates in github pages and their file-based model view. Each paper in the website is associated a markdown file under the _papers folder. Modifications to the key-value pairs in this single file would be reflected to the whole website.

Mandatory keys in a paper description are:

  • layout to be left to paper
  • ref a string acting as a unique identifier
  • title the complete paper title
  • date intended as a publication date (only the year matters)
  • tags a space-separated sequence of tags to classify the paper (see below)
  • authors a string with authors names, separated by comma
  • venue the publication venue (conference, journal name)

Optional keys are:

  • pdf a link to a publicly readable version of the paper
  • code link to the code released with the paper
  • abstract the paper abstract, as a single string
  • bibtex a string for the bibtex entry

The script dblp_to_md.py is a quick and dirt way to generate a skeleton of a markdown file entry from the condensed bibtex as available from DBLP

Available tags

Papers on PCs can be catalogued according to the following tags.

Models:

  • acs: Arithmetic circuits
  • cnets: Cutset networks
  • spns: Sum-Product networks
  • aogs: And/Or graphs
  • pdgs: Probabilistic decision graphs
  • psdds: Probabilistic sentential decision diagrams
  • pcs: Other probabilistic circuits

Algorithms:

  • str-le: Structure learning
  • par-le: Parameter learning
  • comp: Compilation

Inference:

  • mar: Marginal inference
  • map: MAP inference
  • mmap: Marginal MAP inference
  • div: Divergences, IPMs
  • exp: Expectations
  • mom: Moments
  • sam: Sampling
  • app: Approximate inference
  • imp: Imprecise probabilities

Applications:

  • cv: Computer vision
  • nlp: Natural language processing
  • seg: Semantic segmentation
  • act: Activity recognition
  • spe: Speech recognition and reconstruction
  • rob: Robotics
  • bio: Computational biology
  • the: Theory
  • ppl: Probabilistic Programming
  • rep: Representation Learning
  • hw: Hardware
  • sw: Software
  • xai: Explanations
  • misc: Other applications

Thanks

Special thanks to Giuseppe Lobraico who taught me how to deal with the ruby stack behind Jekyll.