{"id":"https://openalex.org/W3035105259","doi":"https://doi.org/10.24963/ijcai.2020/393","title":"Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error","display_name":"Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3035105259","doi":"https://doi.org/10.24963/ijcai.2020/393","mag":"3035105259"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/393","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/393","pdf_url":"https://www.ijcai.org/proceedings/2020/0393.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0393.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078812767","display_name":"Taiji Suzuki","orcid":"https://orcid.org/0000-0003-3459-1016"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]},{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Taiji Suzuki","raw_affiliation_strings":["Center for Advanced Intelligence Project, RIKEN","The University of Tokyo","The University of Tokyo, Japan ,","Center for Advanced Intelligence Project, RIKEN, Japan ,"],"affiliations":[{"raw_affiliation_string":"Center for Advanced Intelligence Project, RIKEN","institution_ids":["https://openalex.org/I4210126580"]},{"raw_affiliation_string":"The University of Tokyo","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, Japan ,","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Center for Advanced Intelligence Project, RIKEN, Japan ,","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088256925","display_name":"Hiroshi Abe","orcid":"https://orcid.org/0000-0002-9711-9715"},"institutions":[{"id":"https://openalex.org/I4210085902","display_name":"Globeride (Japan)","ror":"https://ror.org/0039pzg53","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210085902"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Abe","raw_affiliation_strings":["iPride Co., Ltd","iPride Co., Ltd., Japan ,"],"affiliations":[{"raw_affiliation_string":"iPride Co., Ltd","institution_ids":[]},{"raw_affiliation_string":"iPride Co., Ltd., Japan ,","institution_ids":["https://openalex.org/I4210085902"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065727065","display_name":"Tomoya Murata","orcid":"https://orcid.org/0000-0001-8917-3714"},"institutions":[{"id":"https://openalex.org/I4210165074","display_name":"Information and Mathematical Science and Bioinformatics (Japan)","ror":"https://ror.org/05rgpb984","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210165074"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoya Murata","raw_affiliation_strings":["NTT DATA Mathematical Systems Inc","NTT DATA Mathematical Systems Inc., Japan ,"],"affiliations":[{"raw_affiliation_string":"NTT DATA Mathematical Systems Inc","institution_ids":[]},{"raw_affiliation_string":"NTT DATA Mathematical Systems Inc., Japan ,","institution_ids":["https://openalex.org/I4210165074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114067893","display_name":"Shingo Horiuchi","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shingo Horiuchi","raw_affiliation_strings":["NTT DATA Corporation","NTT Data Corporation, Japan,"],"affiliations":[{"raw_affiliation_string":"NTT DATA Corporation","institution_ids":[]},{"raw_affiliation_string":"NTT Data Corporation, Japan,","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113848608","display_name":"Kotaro Ito","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165074","display_name":"Information and Mathematical Science and Bioinformatics (Japan)","ror":"https://ror.org/05rgpb984","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210165074"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kotaro Ito","raw_affiliation_strings":["NTT DATA Mathematical Systems Inc","NTT DATA Mathematical Systems Inc., Japan ,"],"affiliations":[{"raw_affiliation_string":"NTT DATA Mathematical Systems Inc","institution_ids":[]},{"raw_affiliation_string":"NTT DATA Mathematical Systems Inc., Japan ,","institution_ids":["https://openalex.org/I4210165074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011598215","display_name":"Tokuma Wachi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165074","display_name":"Information and Mathematical Science and Bioinformatics (Japan)","ror":"https://ror.org/05rgpb984","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210165074"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tokuma Wachi","raw_affiliation_strings":["NTT DATA Corporation","NTT DATA Mathematical Systems Inc., Japan ,"],"affiliations":[{"raw_affiliation_string":"NTT DATA Corporation","institution_ids":[]},{"raw_affiliation_string":"NTT DATA Mathematical Systems Inc., Japan ,","institution_ids":["https://openalex.org/I4210165074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069675172","display_name":"So Hirai","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"So Hirai","raw_affiliation_strings":["NTT DATA Corporation","NTT Data Corporation, Japan,"],"affiliations":[{"raw_affiliation_string":"NTT DATA Corporation","institution_ids":[]},{"raw_affiliation_string":"NTT Data Corporation, Japan,","institution_ids":["https://openalex.org/I2251713219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055538886","display_name":"Masatoshi Yukishima","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165074","display_name":"Information and Mathematical Science and Bioinformatics (Japan)","ror":"https://ror.org/05rgpb984","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210165074"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masatoshi Yukishima","raw_affiliation_strings":["NTT DATA Mathematical Systems Inc","NTT DATA Mathematical Systems Inc., Japan ,"],"affiliations":[{"raw_affiliation_string":"NTT DATA Mathematical Systems Inc","institution_ids":[]},{"raw_affiliation_string":"NTT DATA Mathematical Systems Inc., Japan ,","institution_ids":["https://openalex.org/I4210165074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108560731","display_name":"T. Nishimura","orcid":null},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoaki Nishimura","raw_affiliation_strings":["NTT DATA Corporation","NTT Data Corporation, Japan,"],"affiliations":[{"raw_affiliation_string":"NTT DATA Corporation","institution_ids":[]},{"raw_affiliation_string":"NTT Data Corporation, Japan,","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5078812767"],"corresponding_institution_ids":["https://openalex.org/I4210126580","https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":2.6914,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89902604,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2839","last_page":"2846"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9984999895095825,"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":0.9973000288009644,"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/computer-science","display_name":"Computer science","score":0.740760862827301},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6000944972038269},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.5722998380661011},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.5639129877090454},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5506731867790222},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5265175104141235},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5235676169395447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5052734017372131},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.5007715225219727},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.47982168197631836},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.41031062602996826},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23025226593017578}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.740760862827301},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6000944972038269},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5722998380661011},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.5639129877090454},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5506731867790222},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5265175104141235},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5235676169395447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5052734017372131},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5007715225219727},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.47982168197631836},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.41031062602996826},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23025226593017578},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/393","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/393","pdf_url":"https://www.ijcai.org/proceedings/2020/0393.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/393","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/393","pdf_url":"https://www.ijcai.org/proceedings/2020/0393.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G7879866177","display_name":null,"funder_award_id":"CREST","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"},{"id":"https://openalex.org/G8044579487","display_name":null,"funder_award_id":"Japan","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"},{"id":"https://openalex.org/G8672284614","display_name":null,"funder_award_id":"Kaken","funder_id":"https://openalex.org/F4320320912","funder_display_name":"Ministry of Education, Culture, Sports, Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320320912","display_name":"Ministry of Education, Culture, Sports, Science and Technology","ror":"https://ror.org/048rj2z13"},{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3035105259.pdf","grobid_xml":"https://content.openalex.org/works/W3035105259.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W566555209","https://openalex.org/W1686810756","https://openalex.org/W1811750039","https://openalex.org/W2108598243","https://openalex.org/W2144513243","https://openalex.org/W2194775991","https://openalex.org/W2500139799","https://openalex.org/W2798865883","https://openalex.org/W2800415562","https://openalex.org/W2915008147","https://openalex.org/W2950248853","https://openalex.org/W2952825952","https://openalex.org/W2952899695","https://openalex.org/W2962963202","https://openalex.org/W2963000224","https://openalex.org/W2963145730","https://openalex.org/W2963236897","https://openalex.org/W2963363373","https://openalex.org/W2963709899","https://openalex.org/W2963826371","https://openalex.org/W2964233199","https://openalex.org/W2976958311","https://openalex.org/W3035105259","https://openalex.org/W4297738147","https://openalex.org/W4297813615"],"related_works":["https://openalex.org/W4296209631","https://openalex.org/W3097449145","https://openalex.org/W2561617217","https://openalex.org/W2025378473","https://openalex.org/W2373300491","https://openalex.org/W2395294869","https://openalex.org/W2378744544","https://openalex.org/W2355801475","https://openalex.org/W2612632602","https://openalex.org/W2321805087"],"abstract_inverted_index":{"Compression":[0],"techniques":[1],"for":[2,11,31,75],"deep":[3,17,37],"neural":[4],"network":[5],"models":[6],"are":[7],"becoming":[8],"very":[9],"important":[10,30],"the":[12,33,41,94,100,108,112,116,122,140,145,150,167,170,171],"efficient":[13],"execution":[14],"of":[15,25,36,61,96,103,111,139,169],"high-performance":[16],"learning":[18,63],"systems":[19],"on":[20,89],"edge-computing":[21],"devices.":[22],"The":[23],"concept":[24],"model":[26,76,105,142],"compression":[27,55,77,123,151],"is":[28,47,125],"also":[29],"analyzing":[32],"generalization":[34,136],"error":[35,43,137],"learning,":[38],"known":[39],"as":[40],"compression-based":[42],"bound.":[44],"However,":[45],"there":[46],"still":[48],"huge":[49],"gap":[50],"between":[51],"a":[52,71,80,104,134],"practically":[53],"effective":[54],"method":[56,83,156],"and":[57,78,119,143,165],"its":[58],"rigorous":[59],"background":[60],"statistical":[62],"theory.":[64],"To":[65],"resolve":[66],"this":[67,90,129],"issue,":[68],"we":[69,132],"develop":[70],"new":[72,81],"theoretical":[73,163],"framework":[74],"propose":[79],"pruning":[82],"called":[84],"{\\it":[85],"spectral":[86],"pruning}":[87],"based":[88],"framework.":[91],"We":[92,153],"define":[93],"``degrees":[95],"freedom''":[97],"to":[98,157,160],"quantify":[99],"intrinsic":[101],"dimensionality":[102],"by":[106,128,149],"using":[107],"eigenvalue":[109],"distribution":[110],"covariance":[113],"matrix":[114],"across":[115],"internal":[117],"nodes":[118],"show":[120,166],"that":[121],"ability":[124],"essentially":[126],"controlled":[127],"quantity.":[130],"Moreover,":[131],"present":[133],"sharp":[135],"bound":[138],"compressed":[141],"characterize":[144],"bias--variance":[146],"tradeoff":[147],"induced":[148],"procedure.":[152],"apply":[154],"our":[155,162],"several":[158],"datasets":[159],"justify":[161],"analyses":[164],"superiority":[168],"proposed":[172],"method.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
