{"id":"https://openalex.org/W2991648217","doi":null,"title":"Error-Correcting Neural Network.","display_name":"Error-Correcting Neural Network.","publication_year":2019,"publication_date":"2019-11-30","ids":{"openalex":"https://openalex.org/W2991648217","mag":"2991648217"},"language":"en","primary_location":{"id":"mag:2991648217","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1912.00181.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1912.00181.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041567418","display_name":"Yang Song","orcid":"https://orcid.org/0000-0003-1283-1672","observed_orcids":["https://orcid.org/0000-0003-1283-1672","https://orcid.org/0009-0008-7231-4275"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Song","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1283-1672","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029731731","display_name":"Qiyu Kang","orcid":"https://orcid.org/0000-0001-8014-7800","observed_orcids":["https://orcid.org/0000-0001-8014-7800"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiyu Kang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8014-7800","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5046850399","display_name":"Wee Peng Tay","orcid":"https://orcid.org/0000-0002-1543-195X","observed_orcids":["https://orcid.org/0000-0002-1543-195X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wee Peng Tay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"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":0.9990000128746033,"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/T12535","display_name":"Machine Learning and Data Classification","score":9.999999747378752e-05,"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/T10036","display_name":"Advanced Neural Network Applications","score":9.999999747378752e-05,"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-examples","display_name":"adversarial examples","score":0.8389999866485596},{"id":"https://openalex.org/keywords/adversarial-robustness","display_name":"adversarial robustness","score":0.8389999866485596},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"multi-class classification","score":0.847000002861023},{"id":"https://openalex.org/keywords/binary-classifiers","display_name":"binary classifiers","score":0.765999972820282},{"id":"https://openalex.org/keywords/hamming-distance","display_name":"Hamming distance","score":0.8830000162124634},{"id":"https://openalex.org/keywords/classifier-diversity","display_name":"classifier diversity","score":0.9599999785423279},{"id":"https://openalex.org/keywords/end-to-end-learning","display_name":"end-to-end learning","score":0.8560000061988831},{"id":"https://openalex.org/keywords/white-box-attacks","display_name":"white-box attacks","score":0.8859999775886536},{"id":"https://openalex.org/keywords/error-correcting-output-codes","display_name":"error-correcting output codes","score":0.8069999814033508}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7138457298278809},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.6788743734359741},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.6637430191040039},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5864733457565308},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.566325843334198},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5453847646713257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5321696400642395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5289171934127808},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.48712730407714844},{"id":"https://openalex.org/C73150493","wikidata":"https://www.wikidata.org/wiki/Q853922","display_name":"Hamming code","level":4,"score":0.469733327627182},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4656558632850647},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.417472779750824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40447965264320374},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.16217833757400513},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15750426054000854},{"id":"https://openalex.org/C157125643","wikidata":"https://www.wikidata.org/wiki/Q884707","display_name":"Block code","level":3,"score":0.11095613241195679},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.08349159359931946},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2991648217","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1912.00181.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:2991648217","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1912.00181.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1676820704","https://openalex.org/W1989645203","https://openalex.org/W2024060531","https://openalex.org/W2027891831","https://openalex.org/W2099529871","https://openalex.org/W2101276256","https://openalex.org/W2112796928","https://openalex.org/W2129070834","https://openalex.org/W2132420641","https://openalex.org/W2133436028","https://openalex.org/W2149933564","https://openalex.org/W2160815625","https://openalex.org/W2174868984","https://openalex.org/W2180612164","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2311132329","https://openalex.org/W2460937040","https://openalex.org/W2516574342","https://openalex.org/W2531327146","https://openalex.org/W2618043096","https://openalex.org/W2774018344","https://openalex.org/W2781957615","https://openalex.org/W2787496614","https://openalex.org/W2909303050","https://openalex.org/W2912070915","https://openalex.org/W2962699575","https://openalex.org/W2963207607","https://openalex.org/W2963579559","https://openalex.org/W2964153729","https://openalex.org/W2964253222","https://openalex.org/W2970504098","https://openalex.org/W2996058201","https://openalex.org/W3007305010","https://openalex.org/W3118608800"],"related_works":["https://openalex.org/W3159864239","https://openalex.org/W3013971818","https://openalex.org/W3101628297","https://openalex.org/W4407509657","https://openalex.org/W3176745508","https://openalex.org/W4205528202","https://openalex.org/W4323556281","https://openalex.org/W2970065861","https://openalex.org/W4298224792","https://openalex.org/W4409156528"],"abstract_inverted_index":{"Though":[0],"deep":[1],"learning":[2],"has":[3],"been":[4],"applied":[5],"successfully":[6],"in":[7,19,41],"many":[8],"scenarios,":[9],"malicious":[10],"inputs":[11],"with":[12],"human-imperceptible":[13],"perturbations":[14],"can":[15,93],"make":[16],"it":[17],"vulnerable":[18],"real":[20],"applications.":[21],"This":[22],"paper":[23],"proposes":[24],"an":[25,49,112],"error-correcting":[26,140],"neural":[27],"network":[28],"(ECNN)":[29],"that":[30,59,146,154],"combines":[31],"a":[32,55],"set":[33],"of":[34,84,123],"binary":[35,108,127,148],"classifiers":[36,149],"to":[37,53],"combat":[38],"adversarial":[39],"examples":[40],"the":[42,60,72,95,105,124,138,161],"multi":[43],"class":[44,85],"classification":[45,168],"problem.":[46],"To":[47],"build":[48],"ECNN,":[50,118],"we":[51],"propose":[52,111],"design":[54],"code":[56,142],"matrix":[57],"so":[58],"minimum":[61,73],"Hamming":[62],"distance":[63,76],"between":[64,77,107,126],"any":[65,78],"two":[66,69,79,82],"rows":[67],"(i.e.,":[68,81],"codewords)":[70],"and":[71],"shared":[74],"information":[75],"columns":[80],"partitions":[83],"labels)":[86],"are":[87],"simultaneously":[88],"maximized.":[89],"Maximizing":[90],"row":[91],"distances":[92,102],"increase":[94,104],"system":[96],"fault":[97],"tolerance":[98],"while":[99,165],"maximizing":[100],"column":[101],"helps":[103],"diversity":[106,125],"classifiers.":[109,128],"We":[110,151],"end-to-end":[113,130],"training":[114,131],"method":[115],"for":[116],"our":[117,133,155],"which":[119],"allows":[120],"further":[121],"improvement":[122],"The":[129],"renders":[132],"proposed":[134,156],"ECNN":[135,157],"different":[136],"from":[137],"traditional":[139],"output":[141],"(ECOC)":[143],"based":[144],"methods":[145],"train":[147],"independently.":[150],"empirically":[152],"demonstrate":[153],"is":[158],"effective":[159],"against":[160],"state-of-the-art":[162],"white-box":[163],"attacks":[164],"maintaining":[166],"good":[167],"accuracy":[169],"on":[170],"normal":[171],"examples.":[172]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2019-12-05T00:00:00"}
