{"id":"https://openalex.org/W2898781107","doi":"https://doi.org/10.1109/icassp.2019.8682265","title":"Low-rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling","display_name":"Low-rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2898781107","doi":"https://doi.org/10.1109/icassp.2019.8682265","mag":"2898781107"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682265","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682265","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100323305","display_name":"Chao Li","orcid":"https://orcid.org/0009-0004-4147-9537"},"institutions":[{"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":false,"raw_author_name":"Chao Li","raw_affiliation_strings":["RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016846734","display_name":"Zhun Sun","orcid":null},"institutions":[{"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":false,"raw_author_name":"Zhun Sun","raw_affiliation_strings":["RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061696482","display_name":"Jinshi Yu","orcid":"https://orcid.org/0000-0001-7930-5146"},"institutions":[{"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":false,"raw_author_name":"Jinshi Yu","raw_affiliation_strings":["RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101427731","display_name":"Ming Hou","orcid":"https://orcid.org/0000-0001-6539-028X"},"institutions":[{"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":false,"raw_author_name":"Ming Hou","raw_affiliation_strings":["RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083182987","display_name":"Qibin Zhao","orcid":"https://orcid.org/0000-0002-4442-3182"},"institutions":[{"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":false,"raw_author_name":"Qibin Zhao","raw_affiliation_strings":["RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5887,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.57598039,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3022","last_page":"3026"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9904000163078308,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.925599992275238,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7856404185295105},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7124838829040527},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6829661726951599},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6731457114219666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6248771548271179},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5965589284896851},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5690873861312866},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5551189184188843},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.41965028643608093},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3435172140598297},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26028111577033997},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20135611295700073}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7856404185295105},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7124838829040527},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6829661726951599},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6731457114219666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6248771548271179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5965589284896851},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5690873861312866},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5551189184188843},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41965028643608093},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3435172140598297},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26028111577033997},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20135611295700073},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682265","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682265","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W639708223","https://openalex.org/W1798945469","https://openalex.org/W1836465849","https://openalex.org/W1993482030","https://openalex.org/W2008006577","https://openalex.org/W2053923031","https://openalex.org/W2119144962","https://openalex.org/W2119412403","https://openalex.org/W2161591461","https://openalex.org/W2431890537","https://openalex.org/W2516041031","https://openalex.org/W2551156993","https://openalex.org/W2559813832","https://openalex.org/W2613718673","https://openalex.org/W2738900641","https://openalex.org/W2923561138","https://openalex.org/W2949117887","https://openalex.org/W2952505042","https://openalex.org/W2952899695","https://openalex.org/W2962766718","https://openalex.org/W2962988160","https://openalex.org/W2963048316","https://openalex.org/W2964013315","https://openalex.org/W2964299589","https://openalex.org/W3081927587","https://openalex.org/W3118608800","https://openalex.org/W3121797243","https://openalex.org/W4297781745","https://openalex.org/W6602211262","https://openalex.org/W6620707391","https://openalex.org/W6638060716","https://openalex.org/W6638667902","https://openalex.org/W6677580257","https://openalex.org/W6679667936","https://openalex.org/W6683826617","https://openalex.org/W6685627644","https://openalex.org/W6713960836","https://openalex.org/W6717575008","https://openalex.org/W6729942461","https://openalex.org/W6730093588","https://openalex.org/W6741969688","https://openalex.org/W6746466744","https://openalex.org/W6748780714","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2897298721","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W4290987221","https://openalex.org/W2216309014","https://openalex.org/W2569661359","https://openalex.org/W3199841771","https://openalex.org/W4286971555"],"abstract_inverted_index":{"Although":[0],"the":[1,25,29,35,48,59,66,73,80,98,118,122,126,130,137,145,151,160],"convolutional":[2,52,99],"neural":[3],"networks":[4],"(CNNs)":[5],"have":[6],"become":[7],"popular":[8],"for":[9,31,106],"various":[10],"image":[11],"processing":[12],"and":[13],"computer":[14],"vision":[15],"tasks":[16],"recently,":[17],"it":[18],"remains":[19],"a":[20,51,55,107,165],"challenging":[21],"problem":[22],"to":[23],"reduce":[24],"storage":[26],"cost":[27],"of":[28,50,61,121,129,168],"parameters":[30],"resource-limited":[32],"platforms.":[33],"In":[34,113],"previous":[36],"studies,":[37],"tensor":[38,103],"decomposition":[39,104],"(TD)":[40],"has":[41],"achieved":[42],"promising":[43],"compression":[44,169],"performance":[45],"by":[46,96],"embedding":[47],"kernel":[49,67,81],"layer":[53],"into":[54,85],"low-rank":[56,91,127],"subspace.":[57],"However":[58],"employment":[60],"TD":[62],"is":[63],"naively":[64],"on":[65],"or":[68,88],"its":[69],"specified":[70],"variants.":[71],"Unlike":[72],"conventional":[74,161],"approaches,":[75],"this":[76,95],"paper":[77],"shows":[78],"that":[79,136],"can":[82,139],"be":[83,140],"embedded":[84],"more":[86,155],"general":[87],"even":[89,143],"random":[90],"subspaces.":[92],"We":[93],"demonstrate":[94],"compressing":[97],"layers":[100],"via":[101],"randomly-shuffled":[102],"(RsTD)":[105],"standard":[108],"classification":[109,157],"task":[110],"using":[111],"CIFAR-10.":[112],"addition,":[114],"we":[115],"analyze":[116],"how":[117],"spatial":[119],"similarity":[120],"training":[123],"data":[124],"influences":[125],"structure":[128],"kernels.":[131],"The":[132],"experimental":[133],"results":[134],"show":[135],"CNN":[138],"significantly":[141],"compressed":[142],"if":[144],"kernels":[146],"are":[147],"randomly":[148],"shuffled.":[149],"Furthermore,":[150],"RsTD-based":[152],"method":[153],"yields":[154],"stable":[156],"accuracy":[158],"than":[159],"TD-based":[162],"methods":[163],"in":[164],"large":[166],"range":[167],"ratios.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}