{"id":"https://openalex.org/W4406692178","doi":"https://doi.org/10.48550/arxiv.2501.07021","title":"Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning","display_name":"Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning","publication_year":2025,"publication_date":"2025-01-13","ids":{"openalex":"https://openalex.org/W4406692178","doi":"https://doi.org/10.48550/arxiv.2501.07021"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2501.07021","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2501.07021","pdf_url":"https://arxiv.org/pdf/2501.07021","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2501.07021","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101635095","display_name":"Weixin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chen, Weixin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040190424","display_name":"Simon C.H. Yu","orcid":"https://orcid.org/0000-0002-8715-5026"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Simon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041685416","display_name":"Huajie Shao","orcid":"https://orcid.org/0000-0001-7627-5615"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Huajie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067032971","display_name":"Lui Sha","orcid":"https://orcid.org/0000-0002-5578-0791"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sha, Lui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026062552","display_name":"Han Zhao","orcid":"https://orcid.org/0000-0003-0680-9479"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Han","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101635095"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9685999751091003,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9685999751091003,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7557951211929321},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5176090002059937},{"id":"https://openalex.org/keywords/logical-reasoning","display_name":"Logical reasoning","score":0.48775774240493774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47231289744377136},{"id":"https://openalex.org/keywords/logical-conjunction","display_name":"Logical conjunction","score":0.458414763212204},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.09613138437271118}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7557951211929321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5176090002059937},{"id":"https://openalex.org/C43971567","wikidata":"https://www.wikidata.org/wiki/Q3142865","display_name":"Logical reasoning","level":2,"score":0.48775774240493774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47231289744377136},{"id":"https://openalex.org/C21847791","wikidata":"https://www.wikidata.org/wiki/Q191081","display_name":"Logical conjunction","level":2,"score":0.458414763212204},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.09613138437271118}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2501.07021","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2501.07021","pdf_url":"https://arxiv.org/pdf/2501.07021","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2501.07021","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2501.07021","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2501.07021","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2501.07021","pdf_url":"https://arxiv.org/pdf/2501.07021","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4406692178.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2042327395","https://openalex.org/W2366384788","https://openalex.org/W2351550978","https://openalex.org/W4379280870","https://openalex.org/W312725877","https://openalex.org/W2384518852","https://openalex.org/W2467575091","https://openalex.org/W2532166447","https://openalex.org/W2357042771","https://openalex.org/W2379238615"],"abstract_inverted_index":{"End-to-end":[0],"deep":[1],"neural":[2],"networks":[3],"have":[4],"achieved":[5],"remarkable":[6],"success":[7],"across":[8],"various":[9,86],"domains":[10],"but":[11],"are":[12],"often":[13,31],"criticized":[14],"for":[15,85],"their":[16],"lack":[17],"of":[18,75,139,150,185],"interpretability.":[19,192],"While":[20],"post":[21],"hoc":[22],"explanation":[23],"methods":[24],"attempt":[25],"to":[26,33,104],"address":[27],"this":[28],"issue,":[29],"they":[30],"fail":[32],"accurately":[34],"represent":[35],"these":[36,47],"black-box":[37,187],"models,":[38],"resulting":[39],"in":[40],"misleading":[41],"or":[42],"incomplete":[43],"explanations.":[44,162],"To":[45,108,145],"overcome":[46],"challenges,":[48],"we":[49,111,126,152],"propose":[50],"an":[51,72,78,130],"inherently":[52],"transparent":[53],"model":[54],"architecture":[55],"called":[56],"Neural":[57],"Probabilistic":[58],"Circuits":[59],"(NPCs),":[60],"which":[61,82,97],"enable":[62],"compositional":[63],"and":[64,88,122,159,177],"interpretable":[65],"predictions":[66],"through":[67],"logical":[68,99],"reasoning.":[69],"In":[70],"particular,":[71],"NPC":[73],"consists":[74],"two":[76],"modules:":[77],"attribute":[79,118],"recognition":[80],"model,":[81],"predicts":[83],"probabilities":[84],"attributes,":[87],"a":[89,94,113,136,173],"task":[90],"predictor":[91],"built":[92],"on":[93,165],"probabilistic":[95],"circuit,":[96],"enables":[98],"reasoning":[100],"over":[101],"recognized":[102],"attributes":[103],"make":[105],"class":[106],"predictions.":[107],"train":[109],"NPCs,":[110],"introduce":[112],"three-stage":[114],"training":[115],"algorithm":[116],"comprising":[117],"recognition,":[119],"circuit":[120],"construction,":[121],"joint":[123],"optimization.":[124],"Moreover,":[125],"theoretically":[127],"demonstrate":[128,147],"that":[129,170],"NPC's":[131],"error":[132],"is":[133],"upper-bounded":[134],"by":[135],"linear":[137],"combination":[138],"the":[140,148,155,160],"errors":[141],"from":[142],"its":[143],"modules.":[144],"further":[146],"interpretability":[149,176],"NPC,":[151],"provide":[153],"both":[154],"most":[156],"probable":[157],"explanations":[158],"counterfactual":[161],"Empirical":[163],"results":[164,180],"four":[166],"benchmark":[167],"datasets":[168],"show":[169],"NPCs":[171],"strike":[172],"balance":[174],"between":[175],"performance,":[178],"achieving":[179],"competitive":[181],"even":[182],"with":[183],"those":[184],"end-to-end":[186],"models":[188],"while":[189],"providing":[190],"enhanced":[191]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}