{"id":"https://openalex.org/W3158693775","doi":"https://doi.org/10.1109/icpr48806.2021.9413177","title":"Deep transformation models","display_name":"Deep transformation models","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3158693775","doi":"https://doi.org/10.1109/icpr48806.2021.9413177","mag":"3158693775"},"language":"en","primary_location":{"id":"pmh:oai:elib.uni-konstanz.de-htwg:2954","is_oa":false,"landing_page_url":"https://opus.htwg-konstanz.de/frontdoor/index/index/docId/2954","pdf_url":null,"source":{"id":"https://openalex.org/S4306400170","display_name":"URN-Resolver at the German National Library (German National Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2802635041","host_organization_name":"Technische Informationsbibliothek (TIB)","host_organization_lineage":["https://openalex.org/I2802635041"],"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":"doc-type:conferenceObject"},"type":"article","indexed_in":[],"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/A5053625034","display_name":"Beate Sick","orcid":"https://orcid.org/0000-0002-7264-1515"},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]},{"id":"https://openalex.org/I858936495","display_name":"ZHAW Zurich University of Applied Sciences","ror":"https://ror.org/05pmsvm27","country_code":"CH","type":"education","lineage":["https://openalex.org/I858936495"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Sick, Beate","raw_affiliation_strings":["EBPI University of Zurich & IDP Zurich University of Applied Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EBPI University of Zurich & IDP Zurich University of Applied Sciences","institution_ids":["https://openalex.org/I858936495","https://openalex.org/I202697423"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112636335","display_name":"Torsten Hathorn","orcid":null},"institutions":[{"id":"https://openalex.org/I202697423","display_name":"University of Zurich","ror":"https://ror.org/02crff812","country_code":"CH","type":"education","lineage":["https://openalex.org/I202697423"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Hathorn, Torsten","raw_affiliation_strings":["EBPI University of Zurich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EBPI University of Zurich","institution_ids":["https://openalex.org/I202697423"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078032390","display_name":"Oliver D\u00fcrr","orcid":"https://orcid.org/0000-0003-2271-8630"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D\u00fcrr, Oliver","raw_affiliation_strings":["IOS Konstanz University of Applied Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IOS Konstanz University of Applied Sciences","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9962000250816345,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9962000250816345,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9904999732971191,"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/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7480748891830444},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7470281720161438},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.7244179248809814},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7227581143379211},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6370378732681274},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5112624168395996},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5003795623779297},{"id":"https://openalex.org/keywords/data-transformation","display_name":"Data transformation","score":0.4843674600124359},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4686262905597687},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44725799560546875},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4384540915489197},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4265151917934418},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32280591130256653},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1372498869895935},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12925249338150024}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7480748891830444},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7470281720161438},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.7244179248809814},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7227581143379211},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6370378732681274},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5112624168395996},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5003795623779297},{"id":"https://openalex.org/C150670458","wikidata":"https://www.wikidata.org/wiki/Q4272815","display_name":"Data transformation","level":3,"score":0.4843674600124359},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4686262905597687},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44725799560546875},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4384540915489197},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4265151917934418},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32280591130256653},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1372498869895935},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12925249338150024},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:elib.uni-konstanz.de-htwg:2954","is_oa":false,"landing_page_url":"https://opus.htwg-konstanz.de/frontdoor/index/index/docId/2954","pdf_url":null,"source":{"id":"https://openalex.org/S4306400170","display_name":"URN-Resolver at the German National Library (German National Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2802635041","host_organization_name":"Technische Informationsbibliothek (TIB)","host_organization_lineage":["https://openalex.org/I2802635041"],"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":"doc-type:conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W129305155","https://openalex.org/W582134693","https://openalex.org/W1579853615","https://openalex.org/W1719489212","https://openalex.org/W2592232824","https://openalex.org/W2752013927","https://openalex.org/W2786449553","https://openalex.org/W2963090522","https://openalex.org/W2963139417","https://openalex.org/W2963238274","https://openalex.org/W2964059111","https://openalex.org/W2993081745","https://openalex.org/W3035562930","https://openalex.org/W3100456062","https://openalex.org/W3125981752","https://openalex.org/W3150807214","https://openalex.org/W4288280188"],"related_works":["https://openalex.org/W1991093342","https://openalex.org/W2078622645","https://openalex.org/W2170798819","https://openalex.org/W4376309286","https://openalex.org/W2002739602","https://openalex.org/W2345647014","https://openalex.org/W2201192772","https://openalex.org/W3136891595","https://openalex.org/W2535030201","https://openalex.org/W1964819397"],"abstract_inverted_index":{"We":[0,87],"present":[1],"a":[2,31,91,118,178],"deep":[3,65,103,142],"transformation":[4,67,93,100,120],"model":[5,68,94],"for":[6,13,158,169],"probabilistic":[7],"regression.":[8],"Deep":[9],"learning":[10,66,104,143,148],"is":[11,25,56,76,117],"known":[12],"outstandingly":[14],"accurate":[15],"predictions":[16],"on":[17,48,181],"complex":[18,109,170],"data":[19],"but":[20],"in":[21,52],"regression":[22],"tasks":[23],"it":[24,55],"predominantly":[26],"used":[27],"to":[28,58,81,107],"just":[29],"predict":[30,108],"single":[32],"number.":[33],"This":[34],"ignores":[35],"the":[36,49,60,70,77,85,115,127,155],"non-deterministic":[37],"character":[38],"of":[39,114,154],"most":[40,78,159],"tasks.":[41],"Especially":[42],"if":[43],"crucial":[44],"decisions":[45],"are":[46],"based":[47],"predictions,":[50],"like":[51],"medical":[53],"applications,":[54],"essential":[57],"quantify":[59],"prediction":[61],"uncertainty.":[62],"The":[63,112,135],"presented":[64],"estimates":[69],"whole":[71],"conditional":[72],"probability":[73],"distribution,":[74],"which":[75,122,173],"thorough":[79],"way":[80],"capture":[82],"uncertainty":[83],"about":[84],"outcome.":[86],"combine":[88],"ideas":[89],"from":[90,102],"statistical":[92],"(most":[95],"likely":[96],"transformation)":[97],"with":[98,126,140],"recent":[99],"models":[101],"(normalizing":[105],"flows)":[106],"outcome":[110],"distributions.":[111],"core":[113],"method":[116,136,167],"parameterized":[119],"function":[121],"can":[123,137],"be":[124,138],"trained":[125],"usual":[128],"maximum":[129],"likelihood":[130],"framework":[131],"using":[132],"gradient":[133],"descent.":[134],"combined":[139],"existing":[141],"architectures.":[144],"For":[145],"small":[146],"machine":[147],"benchmark":[149],"datasets,":[150],"we":[151,174],"report":[152],"state":[153],"art":[156],"performance":[157],"dataset":[160],"and":[161],"partly":[162],"even":[163],"outperform":[164],"it.":[165],"Our":[166],"works":[168],"input":[171],"data,":[172],"demonstrate":[175],"by":[176],"employing":[177],"CNN":[179],"architecture":[180],"image":[182],"data.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
