{"id":39935155,"url":"https://github.com/arranger1044/probabilistic-circuits","last_synced_at":"2026-01-18T18:34:30.924Z","repository":{"id":39919067,"uuid":"254977584","full_name":"arranger1044/probabilistic-circuits","owner":"arranger1044","description":"  A curated collection of papers on probabilistic circuits, computational graphs encoding tractable probability distributions.","archived":false,"fork":false,"pushed_at":"2024-02-06T03:34:24.000Z","size":255,"stargazers_count":49,"open_issues_count":1,"forks_count":7,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-12-06T21:36:12.278Z","etag":null,"topics":["ai","ml","pc","probabilistic-models","tractable-inference"],"latest_commit_sha":null,"homepage":"https://arranger1044.github.io/probabilistic-circuits/","language":"CSS","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arranger1044.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-04-12T00:03:52.000Z","updated_at":"2024-11-28T19:25:46.000Z","dependencies_parsed_at":"2023-02-16T14:30:37.967Z","dependency_job_id":null,"html_url":"https://github.com/arranger1044/probabilistic-circuits","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/arranger1044/probabilistic-circuits","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arranger1044%2Fprobabilistic-circuits","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arranger1044%2Fprobabilistic-circuits/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arranger1044%2Fprobabilistic-circuits/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arranger1044%2Fprobabilistic-circuits/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arranger1044","download_url":"https://codeload.github.com/arranger1044/probabilistic-circuits/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arranger1044%2Fprobabilistic-circuits/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28547291,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-18T14:59:57.589Z","status":"ssl_error","status_checked_at":"2026-01-18T14:59:46.540Z","response_time":98,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","ml","pc","probabilistic-models","tractable-inference"],"created_at":"2026-01-18T18:34:30.803Z","updated_at":"2026-01-18T18:34:30.910Z","avatar_url":"https://github.com/arranger1044.png","language":"CSS","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Probabilistic Circuits\n\nThis repo contains the source code for the website [https://arranger1044.github.io/probabilistic-circuits/](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.\n\n## License\n\n[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)\n\nAll 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. \n\n## How to contribute\n\n\nTo add, change or remove a paper on the website, please open a [pull request](https://github.com/arranger1044/probabilistic-circuits/pulls)!\n\nThis 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.\n\nMandatory keys in a paper description are:\n  - `layout` to be left to `paper`\n  - `ref` a string acting as a unique identifier\n  - `title` the complete paper title\n  - `date` intended as a publication date (only the year matters)\n  - `tags` a space-separated sequence of tags to classify the paper (see below)\n  - `authors` a string with authors names, separated by comma\n  - `venue` the publication venue (conference, journal name)\n  \nOptional keys are:\n  - `pdf` a link to a publicly readable version of the paper\n  - `code` link to the code released with the paper\n  - `abstract` the paper abstract, as a single string\n  - `bibtex` a string for the bibtex entry\n  \n\nThe 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](https://dblp.org/) \n\n### Available tags\n\nPapers on PCs can be catalogued according to the following tags.\n\nModels:\n  - `acs`: Arithmetic circuits\n  - `cnets`: Cutset networks\n  - `spns`: Sum-Product networks\n  - `aogs`: And/Or graphs\n  - `pdgs`: Probabilistic decision graphs\n  - `psdds`: Probabilistic sentential decision diagrams\n  - `pcs`: Other probabilistic circuits\n  \nAlgorithms: \n  - `str-le`: Structure learning\n  - `par-le`: Parameter learning\n  - `comp`: Compilation\n  \nInference: \n  - `mar`: Marginal inference\n  - `map`: MAP inference\n  - `mmap`: Marginal MAP inference\n  - `div`: Divergences, IPMs\n  - `exp`: Expectations\n  - `mom`: Moments\n  - `sam`: Sampling\n  - `app`: Approximate inference\n  - `imp`: Imprecise probabilities\n  \nApplications: \n  - `cv`: Computer vision\n  - `nlp`: Natural language processing\n  - `seg`: Semantic segmentation\n  - `act`: Activity recognition\n  - `spe`: Speech recognition and reconstruction\n  - `rob`: Robotics\n  - `bio`: Computational biology\n  - `the`: Theory\n  - `ppl`: Probabilistic Programming\n  - `rep`: Representation Learning\n  - `hw`: Hardware\n  - `sw`: Software\n  - `xai`: Explanations\n  - `misc`: Other applications\n\n## Thanks\n\nSpecial thanks to [Giuseppe Lobraico](https://github.com/your) who taught me how to deal with the ruby stack behind Jekyll.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farranger1044%2Fprobabilistic-circuits","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farranger1044%2Fprobabilistic-circuits","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farranger1044%2Fprobabilistic-circuits/lists"}