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

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.

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