{"id":"https://openalex.org/W2573774505","doi":"https://doi.org/10.1162/neco_a_00928","title":"Unifying Adversarial Training Algorithms with Data Gradient Regularization","display_name":"Unifying Adversarial Training Algorithms with Data Gradient Regularization","publication_year":2017,"publication_date":"2017-01-17","ids":{"openalex":"https://openalex.org/W2573774505","doi":"https://doi.org/10.1162/neco_a_00928","mag":"2573774505","pmid":"https://pubmed.ncbi.nlm.nih.gov/28095194"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_00928","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_00928","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5084332360","display_name":"Alexander G. Ororbia","orcid":"https://orcid.org/0000-0002-2590-1310"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alexander G. Ororbia II","raw_affiliation_strings":["Pennsylvania State University, University Park, PA 16802, U.S.A"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA 16802, U.S.A","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005431144","display_name":"Daniel Kifer","orcid":"https://orcid.org/0000-0002-4611-7066"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Daniel Kifer","raw_affiliation_strings":["Pennsylvania State University, University Park, PA 16802, U.S.A"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA 16802, U.S.A","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001294898","display_name":"C. Lee Giles","orcid":"https://orcid.org/0000-0002-1931-585X"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"C. Lee Giles","raw_affiliation_strings":["Pennsylvania State University, University Park, PA 16802, U.S.A"],"affiliations":[{"raw_affiliation_string":"Pennsylvania State University, University Park, PA 16802, U.S.A","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5001294898","https://openalex.org/A5005431144","https://openalex.org/A5084332360"],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":2.5352,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.91574849,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"29","issue":"4","first_page":"867","last_page":"887"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9930999875068665,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/adversarial-system","display_name":"Adversarial system","score":0.9015890955924988},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7943055033683777},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6538553237915039},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5981065630912781},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5669186115264893},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5537925958633423},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4929211139678955},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48222196102142334},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4614601731300354},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43093788623809814},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3867614269256592},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27525508403778076}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9015890955924988},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7943055033683777},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6538553237915039},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5981065630912781},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5669186115264893},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5537925958633423},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4929211139678955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48222196102142334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4614601731300354},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43093788623809814},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3867614269256592},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27525508403778076}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1162/neco_a_00928","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_00928","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:28095194","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28095194","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural computation","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5600000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W769612788","https://openalex.org/W1673923490","https://openalex.org/W1883051450","https://openalex.org/W1883420340","https://openalex.org/W1932198206","https://openalex.org/W2006903949","https://openalex.org/W2110798204","https://openalex.org/W2135520967","https://openalex.org/W2218318129","https://openalex.org/W2230740169","https://openalex.org/W2949608135","https://openalex.org/W2963341057","https://openalex.org/W2990138404","https://openalex.org/W4293580221","https://openalex.org/W4297785279"],"related_works":["https://openalex.org/W2294590153","https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480"],"abstract_inverted_index":{"Many":[0],"previous":[1],"proposals":[2,42],"for":[3],"adversarial":[4,23,32,84,128,139],"training":[5,16,85,140],"of":[6,20,47,68,99,108,113],"deep":[7,66,96],"neural":[8],"nets":[9],"have":[10],"included":[11],"directly":[12],"modifying":[13],"the":[14,69,95,123,143],"gradient,":[15],"on":[17,121],"a":[18,49,65],"mix":[19],"original":[21,124],"and":[22,28,78,105,119,127],"examples,":[24],"using":[25],"contractive":[26,71],"penalties,":[27],"approximately":[29],"optimizing":[30,48],"constrained":[31],"objective":[33,52],"functions.":[34],"In":[35,89],"this":[36],"article,":[37],"we":[38,53,92,131],"show":[39],"that":[40,94,133],"these":[41],"are":[43],"actually":[44],"all":[45],"instances":[46],"general,":[50],"regularized":[51],"call":[54],"DataGrad.":[55],"Our":[56],"proposed":[57],"DataGrad":[58,100,138],"framework,":[59],"which":[60],"can":[61],"be":[62],"viewed":[63],"as":[64,83],"extension":[67],"layerwise":[70],"autoencoder":[72],"penalty,":[73],"cleanly":[74],"simplifies":[75],"prior":[76],"work":[77],"easily":[79],"allows":[80],"extensions":[81],"such":[82],"with":[86,137],"multitask":[87,135],"cues.":[88],"our":[90],"experiments,":[91],"find":[93,132],"gradient":[97],"regularization":[98],"(which":[101],"also":[102],"has":[103],"L1":[104],"L2":[106],"flavors":[107],"regularization)":[109],"outperforms":[110],"alternative":[111],"forms":[112],"regularization,":[114],"including":[115],"classical":[116],"L1,":[117],"L2,":[118],"multitask,":[120],"both":[122],"data":[125],"set":[126],"sets.":[129],"Furthermore,":[130],"combining":[134],"optimization":[136],"results":[141],"in":[142],"most":[144],"robust":[145],"performance.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
