{"id":"https://openalex.org/W2964323046","doi":"https://doi.org/10.1109/icpr.2018.8545379","title":"ReNN: Rule-embedded Neural Networks","display_name":"ReNN: Rule-embedded Neural Networks","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2964323046","doi":"https://doi.org/10.1109/icpr.2018.8545379","mag":"2964323046"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8545379","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017468053","display_name":"Hu Wang","orcid":"https://orcid.org/0000-0002-5974-4499"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hu Wang","raw_affiliation_strings":["Data Analysis and Algorithm Department, LOHAS Technology (Beijing) Corporation Limited, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Analysis and Algorithm Department, LOHAS Technology (Beijing) Corporation Limited, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5017468053"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"824","last_page":"829"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9994999766349792,"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":0.9994999766349792,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.940743625164032},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7674190998077393},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7274242043495178},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7126588821411133},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6629733443260193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5270283818244934},{"id":"https://openalex.org/keywords/economic-shortage","display_name":"Economic shortage","score":0.42240387201309204}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.940743625164032},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7674190998077393},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7274242043495178},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7126588821411133},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6629733443260193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5270283818244934},{"id":"https://openalex.org/C194051981","wikidata":"https://www.wikidata.org/wiki/Q1337691","display_name":"Economic shortage","level":3,"score":0.42240387201309204},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2778137410","wikidata":"https://www.wikidata.org/wiki/Q2732820","display_name":"Government (linguistics)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8545379","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8545379","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335639","display_name":"Institute of Automation, Chinese Academy of Sciences","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1511986666","https://openalex.org/W1582487692","https://openalex.org/W1594355775","https://openalex.org/W1673923490","https://openalex.org/W1825675169","https://openalex.org/W1849277567","https://openalex.org/W1899185266","https://openalex.org/W1903029394","https://openalex.org/W1932198206","https://openalex.org/W1969109407","https://openalex.org/W1989789052","https://openalex.org/W2006922696","https://openalex.org/W2016979930","https://openalex.org/W2020647649","https://openalex.org/W2047693489","https://openalex.org/W2056715049","https://openalex.org/W2057674912","https://openalex.org/W2064355504","https://openalex.org/W2064675550","https://openalex.org/W2095739681","https://openalex.org/W2133564696","https://openalex.org/W2136429434","https://openalex.org/W2153595771","https://openalex.org/W2162273778","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2358986845","https://openalex.org/W2593634001","https://openalex.org/W2613328025","https://openalex.org/W2731010577","https://openalex.org/W2766447205","https://openalex.org/W2781566433","https://openalex.org/W2919115771","https://openalex.org/W2962883557","https://openalex.org/W2963749936","https://openalex.org/W2963996492","https://openalex.org/W2964153729","https://openalex.org/W2964308564","https://openalex.org/W4241489251","https://openalex.org/W4297749952","https://openalex.org/W6635655396","https://openalex.org/W6638389677","https://openalex.org/W6639204139","https://openalex.org/W6664498690","https://openalex.org/W6679434410","https://openalex.org/W6682619722","https://openalex.org/W6684191040","https://openalex.org/W6742162214"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W1986582023","https://openalex.org/W2966829450"],"abstract_inverted_index":{"The":[0,122],"artificial":[1],"neural":[2,104,125,137,149],"network":[3],"shows":[4],"powerful":[5],"ability":[6],"of":[7,16,21,124,143],"inference,":[8],"but":[9],"it":[10],"is":[11],"still":[12],"criticized":[13],"for":[14],"lack":[15],"interpretability":[17],"and":[18,45,72,139,165,167],"prerequisite":[19],"needs":[20],"big":[22],"dataset.":[23],"This":[24],"paper":[25],"proposes":[26],"the":[27,34,54,69,73,78,92,141,148],"Rule-embedded":[28],"Neural":[29],"Network":[30],"(ReNN)":[31],"to":[32,41,57,90,112,146],"overcome":[33],"shortages.":[35],"ReNN":[36,63,93,157,175],"first":[37],"makes":[38,64],"local-based":[39],"inferences":[40,66,155],"detect":[42],"local":[43,55,70,163],"patterns,":[44],"then":[46],"uses":[47],"rules":[48,97],"based":[49],"on":[50],"domain":[51],"knowledge":[52],"about":[53],"patterns":[56,71,164],"generate":[58],"rule-modulated":[59,74],"map.":[60,75],"After":[61],"that,":[62],"global-based":[65],"that":[67],"synthesizes":[68],"To":[76],"solve":[77],"optimization":[79,88],"problem":[80],"caused":[81],"by":[82],"rules,":[83,166],"we":[84,100],"use":[85],"a":[86,180],"two-stage":[87],"strategy":[89],"train":[91],"model.":[94],"By":[95],"introducing":[96],"into":[98],"ReNN,":[99],"can":[101,127,151,158],"strengthen":[102],"traditional":[103],"networks":[105,126,150],"with":[106,114,136,161,179],"long-term":[107,131],"dependencies":[108,132],"which":[109],"are":[110,133],"difficult":[111],"learn":[113],"limited":[115],"empirical":[116],"dataset,":[117],"thus":[118,140,168],"improving":[119],"inference":[120],"accuracy.":[121],"complexity":[123],"be":[128,152,159],"reduced":[129],"since":[130],"not":[134],"modeled":[135],"connections,":[138],"amount":[142],"data":[144],"needed":[145],"optimize":[147],"reduced.":[153],"Besides,":[154],"from":[156],"analyzed":[160],"both":[162],"have":[169],"better":[170],"interpretability.":[171],"In":[172],"this":[173],"paper,":[174],"has":[176],"been":[177],"validated":[178],"time-series":[181],"detection":[182],"problem.":[183]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
