{"id":"https://openalex.org/W3090180249","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207465","title":"Exploring the Correlation Between Random Convolutional Architectures and the Trained Equivalent","display_name":"Exploring the Correlation Between Random Convolutional Architectures and the Trained Equivalent","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090180249","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207465","mag":"3090180249"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5087837333","display_name":"Nicholas Evans","orcid":"https://orcid.org/0000-0003-0893-3713"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Nicholas Evans","raw_affiliation_strings":["Australian National University, Canberra, Australia"],"affiliations":[{"raw_affiliation_string":"Australian National University, Canberra, Australia","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063366658","display_name":"Jo Plested","orcid":null},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jo Plested","raw_affiliation_strings":["Australian National University, Canberra, Australia"],"affiliations":[{"raw_affiliation_string":"Australian National University, Canberra, Australia","institution_ids":["https://openalex.org/I118347636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030379402","display_name":"Tom Gedeon","orcid":"https://orcid.org/0000-0001-8356-4909"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tom Gedeon","raw_affiliation_strings":["Australian National University, Canberra, Australia"],"affiliations":[{"raw_affiliation_string":"Australian National University, Canberra, Australia","institution_ids":["https://openalex.org/I118347636"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087837333"],"corresponding_institution_ids":["https://openalex.org/I118347636"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.10536398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9991999864578247,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7998219728469849},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.7462581396102905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7218735814094543},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.6854121685028076},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.511637270450592},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46718457341194153},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46508142352104187},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4443438947200775},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.4134105443954468},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4011465609073639},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24772554636001587},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17411702871322632}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7998219728469849},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.7462581396102905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7218735814094543},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.6854121685028076},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.511637270450592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46718457341194153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46508142352104187},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4443438947200775},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.4134105443954468},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4011465609073639},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24772554636001587},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17411702871322632},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"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/ijcnn48605.2020.9207465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W78356000","https://openalex.org/W1686810756","https://openalex.org/W2012638612","https://openalex.org/W2026131661","https://openalex.org/W2097117768","https://openalex.org/W2114229561","https://openalex.org/W2155910151","https://openalex.org/W2194775991","https://openalex.org/W2531409750","https://openalex.org/W2553303224","https://openalex.org/W2962746461","https://openalex.org/W2963821229","https://openalex.org/W2964081807","https://openalex.org/W4289752289","https://openalex.org/W4300687381","https://openalex.org/W6603161775","https://openalex.org/W6637373629","https://openalex.org/W6683090604","https://openalex.org/W6729956949","https://openalex.org/W6746582238","https://openalex.org/W6748057086","https://openalex.org/W6753048866","https://openalex.org/W6898611122"],"related_works":["https://openalex.org/W4239286941","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W1868434454","https://openalex.org/W4366985237","https://openalex.org/W2810569973","https://openalex.org/W2128396103","https://openalex.org/W4366984740","https://openalex.org/W4367299891"],"abstract_inverted_index":{"In":[0],"this":[1,30,72],"paper":[2],"we":[3],"explore":[4],"the":[5,17,21,46,76,80,86,105,108,140,143,154],"correlation":[6,31,78,102],"between":[7,79],"Convolutional":[8],"Neural":[9,132],"Network":[10],"(CNN)":[11],"architectures":[12,23,36],"with":[13,24,67],"random":[14],"weights":[15,141,155],"in":[16,119,129],"convolutional":[18],"layers":[19,44,69,97],"to":[20,33,39,45,123],"same":[22],"trained":[25,58],"weights.":[26],"We":[27,61,115],"show":[28],"that":[29,48,64],"extends":[32],"deep":[34],"CNN":[35,88],"of":[37,71,82,107,142,149],"up":[38],"10":[40,94],"or":[41],"even":[42,103],"12":[43,96],"extent":[47],"untrained":[49],"model":[50,59],"accuracy":[51],"could":[52],"be":[53,127],"a":[54,100],"useful":[55,128],"proxy":[56],"for":[57,65,139],"accuracy.":[60,92],"also":[62],"find":[63],"models":[66],"fewer":[68],"much":[70],"relationship":[73],"comes":[74],"from":[75,85],"strong":[77],"number":[81],"features":[83],"output":[84],"final":[87,91,144],"layer":[89,111,146],"and":[90,95],"With":[93],"there":[98],"is":[99,112,147],"moderate":[101],"when":[104],"size":[106],"fully":[109],"connected":[110],"held":[113],"constant.":[114],"anticipate":[116],"our":[117],"findings":[118],"extending":[120],"these":[121],"correlations":[122],"deeper":[124],"networks":[125],"will":[126],"designing":[130],"faster":[131,151],"Architecture":[133],"Search":[134],"(NAS)":[135],"models.":[136],"Analytically":[137],"solving":[138],"prediction":[145],"orders":[148],"magnitude":[150],"than":[152],"training":[153],"via":[156],"backpropagation.":[157]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
