{"id":"https://openalex.org/W4319586466","doi":"https://doi.org/10.1109/dsaa54385.2022.10032334","title":"On Training and Verifying Robust Autoencoders","display_name":"On Training and Verifying Robust Autoencoders","publication_year":2022,"publication_date":"2022-10-13","ids":{"openalex":"https://openalex.org/W4319586466","doi":"https://doi.org/10.1109/dsaa54385.2022.10032334"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa54385.2022.10032334","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032334","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"},"type":"article","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/A5032986841","display_name":"Benedikt B\u00f6ing","orcid":null},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Benedikt Boing","raw_affiliation_strings":["TU Dortmund University,Dortmund,Germany","TU Dortmund University, Dortmund, Germany"],"affiliations":[{"raw_affiliation_string":"TU Dortmund University,Dortmund,Germany","institution_ids":["https://openalex.org/I200332995"]},{"raw_affiliation_string":"TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053906729","display_name":"Emmanuel M\u00fcller","orcid":"https://orcid.org/0000-0002-5409-6875"},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Emmanuel Muller","raw_affiliation_strings":["TU Dortmund University,Dortmund,Germany","TU Dortmund University, Dortmund, Germany"],"affiliations":[{"raw_affiliation_string":"TU Dortmund University,Dortmund,Germany","institution_ids":["https://openalex.org/I200332995"]},{"raw_affiliation_string":"TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5032986841"],"corresponding_institution_ids":["https://openalex.org/I200332995"],"apc_list":null,"apc_paid":null,"fwci":0.2078,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46099978,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9926999807357788,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9896000027656555,"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/robustness","display_name":"Robustness (evolution)","score":0.8596903085708618},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7975654006004333},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6245348453521729},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.596099317073822},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5901747345924377},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47913047671318054},{"id":"https://openalex.org/keywords/formal-verification","display_name":"Formal verification","score":0.4266059398651123},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3577929735183716},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2538173198699951}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8596903085708618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975654006004333},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6245348453521729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.596099317073822},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5901747345924377},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47913047671318054},{"id":"https://openalex.org/C111498074","wikidata":"https://www.wikidata.org/wiki/Q173326","display_name":"Formal verification","level":2,"score":0.4266059398651123},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3577929735183716},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2538173198699951},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa54385.2022.10032334","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/dsaa54385.2022.10032334","pdf_url":null,"source":{"id":"https://openalex.org/S4363608340","display_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W2039632702","https://openalex.org/W2095705004","https://openalex.org/W2100495367","https://openalex.org/W2125967324","https://openalex.org/W2127979711","https://openalex.org/W2144513243","https://openalex.org/W2299467264","https://openalex.org/W2560674852","https://openalex.org/W2594877703","https://openalex.org/W2743138268","https://openalex.org/W2747329762","https://openalex.org/W2890472662","https://openalex.org/W2891698621","https://openalex.org/W2957311447","https://openalex.org/W2963054787","https://openalex.org/W2997574889","https://openalex.org/W3131972177","https://openalex.org/W3191453585","https://openalex.org/W4205451017","https://openalex.org/W4249885861","https://openalex.org/W4293846201","https://openalex.org/W6637162671","https://openalex.org/W6674330103","https://openalex.org/W6678914141","https://openalex.org/W6681096077","https://openalex.org/W6681151457","https://openalex.org/W6750745550","https://openalex.org/W6751834733","https://openalex.org/W6754313168","https://openalex.org/W6754543342","https://openalex.org/W6754619240","https://openalex.org/W6755331340"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W2669956259","https://openalex.org/W4249005693"],"abstract_inverted_index":{"Autoencoders":[0],"have":[1],"become":[2],"ubiquitous":[3],"in":[4],"machine":[5],"learning":[6],"thanks":[7],"to":[8,19,87,98,104,128,140],"their":[9,26],"broad":[10],"range":[11],"of":[12,55,62,117],"applications.":[13],"Therefore":[14,90],"we":[15,46,57,91,113],"must":[16],"be":[17],"able":[18],"formally":[20],"state":[21],"and":[22,103,122],"verify":[23],"properties":[24],"about":[25],"behaviour":[27],"such":[28],"as":[29],"denoising":[30],"or":[31],"robustness.":[32],"However,":[33],"so":[34],"far":[35],"formal":[36,50],"verification":[37,136],"for":[38,53,67,109],"autoencoders":[39,68],"has":[40],"almost":[41],"not":[42,84],"been":[43],"addressed.":[44],"Thus":[45],"introduce":[47],"the":[48,64,81,100,106,115,118],"first":[49],"problem":[51,120],"specification":[52,121],"robustness":[54,65,102],"autoencoders.Moreover":[56],"give":[58],"a":[59,93],"framework":[60],"capable":[61],"proving":[63],"property":[66],"based":[69],"on":[70],"SMT":[71,76],"solvers.":[72],"Yet,":[73],"because":[74],"these":[75],"solvers":[77],"are":[78],"notoriously":[79],"slow,":[80],"approach":[82],"does":[83],"scale":[85],"up":[86,139],"larger":[88],"autoencoders.":[89],"describe":[92],"regularization":[94,126],"scheme":[95,127],"aimed":[96],"both":[97],"increase":[99],"autoencoder\u2019s":[101],"decrease":[105],"time":[107,137],"required":[108],"verification.In":[110],"our":[111,124,134],"experiments":[112],"highlight":[114],"use":[116],"new":[119],"compare":[123],"proposed":[125],"other,":[129],"already":[130],"existing":[131],"ones.":[132],"Using":[133],"approach,":[135],"becomes":[138],"21":[141],"times":[142],"faster.":[143]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
