{"id":"https://openalex.org/W4411203063","doi":"https://doi.org/10.1109/access.2025.3578926","title":"An Approach to Finding a Robust Deep Learning Model","display_name":"An Approach to Finding a Robust Deep Learning Model","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4411203063","doi":"https://doi.org/10.1109/access.2025.3578926"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3578926","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3578926","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3578926","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110872942","display_name":"A. S. Boldyrev","orcid":"https://orcid.org/0000-0002-7872-6819"},"institutions":[{"id":"https://openalex.org/I118501908","display_name":"National Research University Higher School of Economics","ror":"https://ror.org/055f7t516","country_code":"RU","type":"education","lineage":["https://openalex.org/I118501908"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Alexey Boldyrev","raw_affiliation_strings":["Laboratory of Methods for Big Data Analysis, HSE University, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Laboratory of Methods for Big Data Analysis, HSE University, Moscow, Russia","institution_ids":["https://openalex.org/I118501908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101073608","display_name":"Fedor Ratnikov","orcid":null},"institutions":[{"id":"https://openalex.org/I118501908","display_name":"National Research University Higher School of Economics","ror":"https://ror.org/055f7t516","country_code":"RU","type":"education","lineage":["https://openalex.org/I118501908"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Fedor Ratnikov","raw_affiliation_strings":["Laboratory of Methods for Big Data Analysis, HSE University, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Laboratory of Methods for Big Data Analysis, HSE University, Moscow, Russia","institution_ids":["https://openalex.org/I118501908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104295224","display_name":"Andrey Shevelev","orcid":null},"institutions":[{"id":"https://openalex.org/I118501908","display_name":"National Research University Higher School of Economics","ror":"https://ror.org/055f7t516","country_code":"RU","type":"education","lineage":["https://openalex.org/I118501908"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Andrey Shevelev","raw_affiliation_strings":["Laboratory of Methods for Big Data Analysis, HSE University, Moscow, Russia"],"affiliations":[{"raw_affiliation_string":"Laboratory of Methods for Big Data Analysis, HSE University, Moscow, Russia","institution_ids":["https://openalex.org/I118501908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110872942"],"corresponding_institution_ids":["https://openalex.org/I118501908"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.7968,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.9334728,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"13","issue":null,"first_page":"102390","last_page":"102406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14470","display_name":"Advanced Data Processing Techniques","score":0.24330000579357147,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14470","display_name":"Advanced Data Processing Techniques","score":0.24330000579357147,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6727561354637146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4954017400741577},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4639410972595215}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6727561354637146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4954017400741577},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4639410972595215}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3578926","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3578926","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9bcdb50d9f89431a8d99391d46e369cb","is_oa":true,"landing_page_url":"https://doaj.org/article/9bcdb50d9f89431a8d99391d46e369cb","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 102390-102406 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3578926","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3578926","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2928192979","display_name":null,"funder_award_id":"000000D730321P5Q0002","funder_id":"https://openalex.org/F4320319755","funder_display_name":"Analytical Center for the Government of the Russian Federation"}],"funders":[{"id":"https://openalex.org/F4320319755","display_name":"Analytical Center for the Government of the Russian Federation","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2010478558","https://openalex.org/W2022939780","https://openalex.org/W2034996255","https://openalex.org/W2087556827","https://openalex.org/W2094132535","https://openalex.org/W2108598243","https://openalex.org/W2128158076","https://openalex.org/W2194775991","https://openalex.org/W2612252966","https://openalex.org/W2809496393","https://openalex.org/W2949676527","https://openalex.org/W2966284335","https://openalex.org/W3035620341","https://openalex.org/W3037492894","https://openalex.org/W3084083219","https://openalex.org/W3094502228","https://openalex.org/W3108981297","https://openalex.org/W3121606938","https://openalex.org/W3164008977","https://openalex.org/W3191453585","https://openalex.org/W4220974656","https://openalex.org/W4238202755","https://openalex.org/W4242989628","https://openalex.org/W4320930577","https://openalex.org/W4323644307","https://openalex.org/W4378070033","https://openalex.org/W4381855479","https://openalex.org/W4392289380","https://openalex.org/W6600284362","https://openalex.org/W6631190155","https://openalex.org/W6638214083","https://openalex.org/W6675216202","https://openalex.org/W6681435938","https://openalex.org/W6685562342","https://openalex.org/W6735767304","https://openalex.org/W6745535076","https://openalex.org/W6755977528","https://openalex.org/W6757107679","https://openalex.org/W6757817989","https://openalex.org/W6759402996","https://openalex.org/W6760266475","https://openalex.org/W6761030284","https://openalex.org/W6771859737","https://openalex.org/W6772129204","https://openalex.org/W6780095956","https://openalex.org/W6780666095","https://openalex.org/W6786798588","https://openalex.org/W6843902755","https://openalex.org/W6852772105","https://openalex.org/W7027645278","https://openalex.org/W7058019785"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"The":[0],"rapid":[1],"development":[2],"of":[3,15,18,26,90,97,109,124,136,149],"machine":[4,72],"learning":[5,73,99,151],"(ML)":[6],"and":[7,68,113,126,143],"artificial":[8],"intelligence":[9],"(AI)":[10],"applications":[11],"requires":[12],"the":[13,24,81,88,95,134,147],"training":[14,27,137],"large":[16],"numbers":[17],"models.":[19,100,152],"This":[20,53,85],"growing":[21],"demand":[22],"highlights":[23],"importance":[25],"models":[28,107],"without":[29],"human":[30],"supervision,":[31],"while":[32],"ensuring":[33],"that":[34,76],"their":[35,122],"predictions":[36],"are":[37],"reliable.":[38],"In":[39],"response":[40],"to":[41,70,93,121],"this":[42,102,130],"need,":[43],"we":[44,104,132],"propose":[45],"a":[46,57,64,110],"novel":[47],"approach":[48,92],"for":[49,80],"determining":[50],"model":[51,59,140],"robustness.":[52],"approach,":[54],"supplemented":[55],"with":[56],"proposed":[58],"selection":[60],"algorithm":[61],"designed":[62],"as":[63],"meta-algorithm,":[65],"is":[66,78],"versatile":[67],"applicable":[69],"any":[71],"model,":[74],"provided":[75],"it":[77],"appropriate":[79],"task":[82],"at":[83],"hand.":[84],"study":[86,105],"demonstrates":[87],"application":[89],"our":[91],"evaluate":[94],"robustness":[96,148],"deep":[98,150],"To":[101],"end,":[103],"small":[106],"composed":[108],"few":[111],"convolutional":[112],"fully":[114],"connected":[115],"layers,":[116],"using":[117],"common":[118],"optimizers":[119],"due":[120],"ease":[123],"interpretation":[125],"computational":[127],"efficiency.":[128],"Within":[129],"framework,":[131],"address":[133],"influence":[135],"sample":[138],"size,":[139],"weight":[141],"initialization,":[142],"inductive":[144],"bias":[145],"on":[146]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}