{"id":"https://openalex.org/W7105668533","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228677","title":"Initializing Complex-Valued Neural Networks from their Pretrained Real-Valued Counterparts","display_name":"Initializing Complex-Valued Neural Networks from their Pretrained Real-Valued Counterparts","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105668533","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228677"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228677","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228677","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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":null,"display_name":"Florian Eilers","orcid":null},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Florian Eilers","raw_affiliation_strings":["University of M&#x00FC;nster,Department of Computer Science,M&#x00FC;nster,Germany"],"affiliations":[{"raw_affiliation_string":"University of M&#x00FC;nster,Department of Computer Science,M&#x00FC;nster,Germany","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xiaoyi Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xiaoyi Jiang","raw_affiliation_strings":["University of M&#x00FC;nster,Department of Computer Science,M&#x00FC;nster,Germany"],"affiliations":[{"raw_affiliation_string":"University of M&#x00FC;nster,Department of Computer Science,M&#x00FC;nster,Germany","institution_ids":["https://openalex.org/I138801177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I138801177"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76984889,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.2651999890804291,"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":0.2651999890804291,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.09799999743700027,"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/T10320","display_name":"Neural Networks and Applications","score":0.09030000120401382,"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":0.7096999883651733},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.6389999985694885},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.47839999198913574},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.45820000767707825},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3846000134944916},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.30889999866485596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7572000026702881},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7096999883651733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6410999894142151},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6389999985694885},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.47839999198913574},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.45820000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4553000032901764},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228677","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228677","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W93167102","https://openalex.org/W1677182931","https://openalex.org/W1998924366","https://openalex.org/W2108598243","https://openalex.org/W2134563598","https://openalex.org/W2194775991","https://openalex.org/W2346062110","https://openalex.org/W2533598788","https://openalex.org/W2772723798","https://openalex.org/W2804935296","https://openalex.org/W2806857275","https://openalex.org/W2991391304","https://openalex.org/W2998874549","https://openalex.org/W3015651375","https://openalex.org/W3037264106","https://openalex.org/W3096408984","https://openalex.org/W3121141908","https://openalex.org/W3137984567","https://openalex.org/W4200453770","https://openalex.org/W4212802476","https://openalex.org/W4289654500","https://openalex.org/W4312443924","https://openalex.org/W4319993388","https://openalex.org/W4321374355","https://openalex.org/W4365801652","https://openalex.org/W4375850652","https://openalex.org/W4379472738","https://openalex.org/W4386825203","https://openalex.org/W4394988986","https://openalex.org/W4394998532","https://openalex.org/W4402750836","https://openalex.org/W4405293826","https://openalex.org/W4414236687"],"related_works":[],"abstract_inverted_index":{"Complex-Valued":[0,60,72],"Neural":[1,61,73],"Networks":[2,62,74],"are":[3],"a":[4],"rising":[5],"field":[6],"in":[7,69],"research":[8],"and":[9],"applications.":[10,88],"However,":[11],"the":[12,28,59],"lack":[13],"of":[14,30],"pretrained":[15,33,45],"complex-valued":[16,50],"models":[17,34,47],"complicates":[18],"their":[19,77,81],"training":[20,58],"process,":[21],"since":[22],"users":[23],"can":[24,75],"not":[25],"benefit":[26],"from":[27,63],"advantages":[29],"having":[31],"large":[32],"publicly":[35],"available.":[36],"We":[37,52],"present":[38],"three":[39],"methods":[40],"to":[41,48],"utilize":[42],"readily":[43],"available":[44],"real-valued":[46,78],"initialize":[49],"models.":[51],"show":[53,67],"that":[54,68],"this":[55],"consistently":[56],"outperforms":[57],"scratch.":[64],"Additionally,":[65],"we":[66],"some":[70],"scenarios":[71],"outperform":[76],"counterparts,":[79],"confirming":[80],"suitability":[82],"as":[83],"an":[84],"alternative":[85],"for":[86],"various":[87]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-14T00:00:00"}
