{"id":"https://openalex.org/W2794239932","doi":"https://doi.org/10.1109/ccwc.2018.8301749","title":"The effect of weight errors on neural networks","display_name":"The effect of weight errors on neural networks","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2794239932","doi":"https://doi.org/10.1109/ccwc.2018.8301749","mag":"2794239932"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc.2018.8301749","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc.2018.8301749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5088372642","display_name":"Austin P. Arechiga","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Austin P. Arechiga","raw_affiliation_strings":["Hume Center for National Security and Technology, Virginia Polytechnic Institute and State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hume Center for National Security and Technology, Virginia Polytechnic Institute and State University","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011390669","display_name":"Alan J. Michaels","orcid":"https://orcid.org/0000-0003-2437-3410"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alan J. Michaels","raw_affiliation_strings":["Hume Center for National Security and Technology, Virginia Polytechnic Institute and State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hume Center for National Security and Technology, Virginia Polytechnic Institute and State University","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":1.3582,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.85865529,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"190","last_page":"196"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9901999831199646,"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6975586414337158},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6718087792396545},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46048200130462646}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6975586414337158},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6718087792396545},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46048200130462646}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc.2018.8301749","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc.2018.8301749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)","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":17,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2040870580","https://openalex.org/W2043483139","https://openalex.org/W2107558051","https://openalex.org/W2112796928","https://openalex.org/W2114027591","https://openalex.org/W2117539524","https://openalex.org/W2132424367","https://openalex.org/W2156387975","https://openalex.org/W2157116240","https://openalex.org/W2163605009","https://openalex.org/W2167677193","https://openalex.org/W2575696673","https://openalex.org/W2618530766","https://openalex.org/W6637373629","https://openalex.org/W6682889407","https://openalex.org/W6732805799"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2386387936","https://openalex.org/W3107474891","https://openalex.org/W1629725936","https://openalex.org/W3001020386","https://openalex.org/W644753246","https://openalex.org/W2159443810"],"abstract_inverted_index":{"Neural":[0,69],"networks":[1,15,52,66,97,128,141,151,155,163],"have":[2],"moved":[3],"beyond":[4],"theoretical":[5],"equations":[6],"and":[7,32,67,101,115,152],"into":[8],"real-world":[9,20],"applications.":[10],"In":[11],"order":[12],"for":[13,153],"neural":[14,41,51,65,112],"to":[16,105,117,133],"be":[17,29],"useful":[18],"in":[19,24,87,125],"environments,":[21],"their":[22],"reliability":[23],"non-ideal":[25],"computing":[26],"conditions":[27],"must":[28],"understood.":[30],"Cyber":[31],"environmental":[33],"threats":[34],"can":[35,53],"both":[36],"affect":[37],"the":[38,59,90,96],"execution":[39],"of":[40,61,95],"network":[42,113],"programs,":[43],"but":[44],"little":[45],"is":[46,98],"known":[47],"about":[48],"how":[49],"well":[50],"withstand":[54],"attack.":[55],"This":[56],"paper":[57],"examines":[58],"robustness":[60,116],"Multilayer":[62],"Perceptron":[63],"(MLP)":[64],"Convolutional":[68],"Networks":[70],"(CNNs)":[71],"against":[72],"weight":[73,118,134],"errors.":[74,119],"Specifically,":[75],"Single":[76],"Event":[77],"Upset":[78],"(SEU)":[79],"style":[80],"errors":[81,135],"such":[82],"as":[83],"individual":[84],"bit":[85,103],"flips":[86,104],"memory":[88],"are":[89,129,145,159],"focus.":[91],"The":[92,120],"classification":[93],"accuracy":[94],"tested":[99],"before":[100],"after":[102],"see":[106],"if":[107],"there's":[108],"any":[109],"correlation":[110],"between":[111],"architecture":[114],"experimental":[121],"results":[122],"show":[123],"that":[124],"general":[126],"MLP":[127],"much":[130],"more":[131,143,146,160],"robust":[132,147,161],"than":[136,148,162],"CNNs.":[137],"For":[138],"MLPs":[139],"larger":[140,157],"with":[142,156,164],"layers":[144],"smaller,":[149],"shallower":[150],"CNNs":[154],"kernels":[158],"smaller":[165],"kernels.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}