乐胖代购免代理版

{"id":"https://openalex.org/W4392693791","doi":"https://doi.org/10.1007/s10044-023-01208-1","title":"Understanding the limitations of self-supervised learning for tabular anomaly detection","display_name":"Understanding the limitations of self-supervised learning for tabular anomaly detection","publication_year":2024,"publication_date":"2024-03-12","ids":{"openalex":"https://openalex.org/W4392693791","doi":"https://doi.org/10.1007/s10044-023-01208-1"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10044-023-01208-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10044-023-01208-1.pdf","source":{"id":"https://openalex.org/S45497385","display_name":"Pattern Analysis and Applications","issn_l":"1433-7541","issn":["1433-7541","1433-755X"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10044-023-01208-1.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017595236","display_name":"T. Kimberly","orcid":"https://orcid.org/0000-0001-6315-1939"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kimberly T. Mai","raw_affiliation_strings":["Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088820022","display_name":"T. Davies","orcid":"https://orcid.org/0000-0002-9677-2579"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Toby Davies","raw_affiliation_strings":["Department of Security and Crime Science, University College London, Gower Street, London, WC1E 6BT, UK"],"affiliations":[{"raw_affiliation_string":"Department of Security and Crime Science, University College London, Gower Street, London, WC1E 6BT, UK","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056135069","display_name":"Lewis D. Griffin","orcid":"https://orcid.org/0000-0001-6286-2018"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Lewis D. Griffin","raw_affiliation_strings":["Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK","institution_ids":["https://openalex.org/I45129253"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990,"provenance":"doaj"},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990,"provenance":"doaj"},"fwci":0.0,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":82},"biblio":{"volume":"27","issue":"2","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.997,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9902,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6122691},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5601483},{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.4928491},{"id":"https://openalex.org/keywords/pretext","display_name":"Pretext","score":0.47781208}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8848855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7560378},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6695961},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6122691},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5863061},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5601483},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5004995},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4928491},{"id":"https://openalex.org/C2779627259","wikidata":"https://www.wikidata.org/wiki/Q779763","display_name":"Pretext","level":3,"score":0.47781208},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47025144},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4431337},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10044-023-01208-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10044-023-01208-1.pdf","source":{"id":"https://openalex.org/S45497385","display_name":"Pattern Analysis and Applications","issn_l":"1433-7541","issn":["1433-7541","1433-755X"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10044-023-01208-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10044-023-01208-1.pdf","source":{"id":"https://openalex.org/S45497385","display_name":"Pattern Analysis and Applications","issn_l":"1433-7541","issn":["1433-7541","1433-755X"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319965","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Springer Nature","Springer Science+Business Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[{"score":0.74,"id":"https://metadata.un.org/sdg/4","display_name":"Quality education"}],"grants":[{"funder":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council","award_id":"EP/R513143/1"}],"datasets":[],"versions":[],"referenced_works_count":26,"referenced_works":["https://openalex.org/W2122646361","https://openalex.org/W2171604933","https://openalex.org/W2194775991","https://openalex.org/W2296719434","https://openalex.org/W2321533354","https://openalex.org/W2326925005","https://openalex.org/W2954540134","https://openalex.org/W2962739339","https://openalex.org/W2963420272","https://openalex.org/W2997591727","https://openalex.org/W2998702515","https://openalex.org/W3129166376","https://openalex.org/W3134961575","https://openalex.org/W3153838899","https://openalex.org/W3158714121","https://openalex.org/W3166898278","https://openalex.org/W3175716777","https://openalex.org/W4223945106","https://openalex.org/W4254182148","https://openalex.org/W4312776478","https://openalex.org/W4313156423","https://openalex.org/W4320481735","https://openalex.org/W4366552064","https://openalex.org/W4367000428","https://openalex.org/W4379469310","https://openalex.org/W4385849075"],"related_works":["https://openalex.org/W4377864969","https://openalex.org/W4300558037","https://openalex.org/W4290647774","https://openalex.org/W3210364259","https://openalex.org/W3207797160","https://openalex.org/W3189286258","https://openalex.org/W3120251014","https://openalex.org/W2912112202","https://openalex.org/W2806741695","https://openalex.org/W2667207928"],"abstract_inverted_index":{"Abstract":[0],"While":[1],"self-supervised":[2],"learning":[3],"has":[4],"improved":[5],"anomaly":[6,34,66,94],"detection":[7,67],"in":[8],"computer":[9],"vision":[10],"and":[11],"natural":[12],"language":[13],"processing,":[14],"it":[15],"is":[16,52,81],"unclear":[17],"whether":[18],"tabular":[19,33,65],"data":[20],"can":[21,108],"benefit":[22],"from":[23,60],"it.":[24],"This":[25],"paper":[26],"explores":[27],"the":[28,53,72,76,91,104],"limitations":[29],"of":[30,75,93,103],"self-supervision":[31,61],"for":[32],"detection.":[35],"We":[36,78],"conduct":[37],"several":[38],"experiments":[39],"spanning":[40],"various":[41],"pretext":[42],"tasks":[43],"on":[44],"26":[45],"benchmark":[46],"datasets":[47],"to":[48,70,83],"understand":[49],"why":[50],"this":[51,80],"case.":[54],"Our":[55],"results":[56],"confirm":[57],"representations":[58,74],"derived":[59],"do":[62],"not":[63],"improve":[64],"performance":[68],"compared":[69],"using":[71,100],"raw":[73],"data.":[77],"show":[79],"due":[82],"neural":[84,105],"networks":[85],"introducing":[86],"irrelevant":[87],"features,":[88],"which":[89],"reduces":[90],"effectiveness":[92],"detectors.":[95],"However,":[96],"we":[97],"demonstrate":[98],"that":[99],"a":[101],"subspace":[102],"network\u2019s":[106],"representation":[107],"recover":[109],"performance.":[110]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4392693791","counts_by_year":[],"updated_date":"2025-01-21T11:19:58.818946","created_date":"2024-03-13"}