{"id":"https://openalex.org/W3109101583","doi":"https://doi.org/10.1145/3400286.3418258","title":"Kernel-controlled DQN based CNN Pruning for Model Compression and Acceleration","display_name":"Kernel-controlled DQN based CNN Pruning for Model Compression and Acceleration","publication_year":2020,"publication_date":"2020-10-13","ids":{"openalex":"https://openalex.org/W3109101583","doi":"https://doi.org/10.1145/3400286.3418258","mag":"3109101583"},"language":"en","primary_location":{"id":"doi:10.1145/3400286.3418258","doi":"https://doi.org/10.1145/3400286.3418258","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3400286.3418258","pdf_url":null,"source":{"id":"https://openalex.org/S7407086765","display_name":"ACM Conference on Research in Adaptive and Convergent Systems (RACS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"},"parent_works":[],"lineage":["https://openalex.org/W3109101583"],"child_works_count":0,"type":"conference-paper","is_traditional":true,"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/A5023393174","display_name":"Romancha Khatri","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Romancha Khatri","raw_affiliation_strings":["South Dakota State University, Brookings, SD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South Dakota State University, Brookings, SD","institution_ids":["https://openalex.org/I177156846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000106707","display_name":"Kwanghee Won","orcid":"https://orcid.org/0000-0003-1717-6881","observed_orcids":["https://orcid.org/0000-0003-1717-6881"]},"institutions":[{"id":"https://openalex.org/I177156846","display_name":"South Dakota State University","ror":"https://ror.org/015jmes13","country_code":"US","type":"education","lineage":["https://openalex.org/I177156846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kwanghee Won","raw_affiliation_strings":["South Dakota State University, Brookings, SD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South Dakota State University, Brookings, SD","institution_ids":["https://openalex.org/I177156846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I177156846"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"36","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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/T12535","display_name":"Machine Learning and Data Classification","score":9.999999747378752e-05,"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":0.0,"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-pruning","display_name":"convolutional neural network pruning","score":0.859000027179718},{"id":"https://openalex.org/keywords/model-compression","display_name":"model compression","score":0.7269999980926514},{"id":"https://openalex.org/keywords/deep-q-network","display_name":"deep Q-network","score":0.8220000267028809},{"id":"https://openalex.org/keywords/deep-reinforcement-learning","display_name":"deep reinforcement learning","score":0.7260000109672546},{"id":"https://openalex.org/keywords/vgg","display_name":"VGG","score":0.6299999952316284},{"id":"https://openalex.org/keywords/model-acceleration","display_name":"model acceleration","score":0.5320000052452087},{"id":"https://openalex.org/keywords/exploration-exploitation-trade-off","display_name":"exploration-exploitation balance","score":0.6980000138282776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7716943025588989},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5963812470436096},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5782188773155212},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5739774107933044},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5713368654251099},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.4952374994754791},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.4928417205810547},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44373390078544617},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4413217008113861},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43267521262168884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4231809973716736},{"id":"https://openalex.org/C94835093","wikidata":"https://www.wikidata.org/wiki/Q3113333","display_name":"Data compression ratio","level":5,"score":0.42152926325798035},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.22271960973739624},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1464434266090393},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08264696598052979},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.07712283730506897},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"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/C511840579","wikidata":"https://www.wikidata.org/wiki/Q12757","display_name":"Internal combustion engine","level":2,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3400286.3418258","doi":"https://doi.org/10.1145/3400286.3418258","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3400286.3418258","pdf_url":null,"source":{"id":"https://openalex.org/S7407086765","display_name":"ACM Conference on Research in Adaptive and Convergent Systems (RACS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Research in Adaptive and Convergent Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:openprairie.sdstate.edu:etd-5066","doi":null,"is_oa":false,"landing_page_url":"https://openprairie.sdstate.edu/etd/4105","pdf_url":null,"source":{"id":"https://openalex.org/S4377196457","display_name":"Open PRAIRIE (South Dakota State University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I177156846","host_organization_name":"South Dakota State University","host_organization_lineage":["https://openalex.org/I177156846"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electronic Theses and Dissertations","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2004915807","https://openalex.org/W2007339694","https://openalex.org/W2024093481","https://openalex.org/W2081860495","https://openalex.org/W2119144962","https://openalex.org/W2145339207","https://openalex.org/W2194775991","https://openalex.org/W2756085244","https://openalex.org/W2766839578","https://openalex.org/W2884367402","https://openalex.org/W2886851211","https://openalex.org/W2964299589","https://openalex.org/W3201902125","https://openalex.org/W6819060087"],"related_works":["https://openalex.org/W2993389083","https://openalex.org/W2841402626","https://openalex.org/W2990368023","https://openalex.org/W3201902125","https://openalex.org/W4313890041","https://openalex.org/W2785761580","https://openalex.org/W3174616979","https://openalex.org/W4394586017","https://openalex.org/W4388906338","https://openalex.org/W4405869745"],"abstract_inverted_index":{"Apart":[0],"from":[1],"the":[2,4,18,40,96,110,145,182,196,207,210,216,222],"accuracy,":[3,83],"size":[5],"of":[6,20,147,150,159,162,176,184,206,231,237,253],"Convolutional":[7],"Neural":[8],"Networks":[9],"(CNN)":[10],"model":[11,91,111,130,193,218,246],"is":[12,51],"another":[13],"principal":[14],"factor":[15],"for":[16,127],"facilitating":[17],"deployment":[19],"models":[21],"on":[22,75,115,117,239,243],"memory,":[23],"power":[24],"and":[25,43,53,86,248],"budget":[26],"constrained":[27],"devices.":[28],"Conventional":[29],"compression":[30,69,84,112],"techniques":[31],"require":[32],"human":[33],"expert":[34],"to":[35,38,100,104,132,154,221],"setup":[36],"parameters":[37],"explore":[39],"design":[41],"space":[42],"iterative":[44],"based":[45,67,242],"pruning":[46],"requires":[47],"heavy":[48],"training":[49,151],"which":[50,70,107],"sub-optimal":[52],"time":[54],"consuming.":[55],"Given":[56],"a":[57,143],"CNN":[58],"model,":[59,208],"we":[60],"propose":[61],"deep":[62],"reinforcement":[63],"learning":[64],"[8]":[65],"DQN":[66,90],"automated":[68],"effectively":[71],"turned":[72],"off":[73],"kernels":[74,98,163,177,238],"each":[76],"layer":[77],"by":[78,171,198,224],"observing":[79],"its":[80],"significance.":[81],"Observing":[82],"ratio":[85],"convergence":[87],"rate,":[88],"proposed":[89,189,212],"can":[92,123,194,219],"automatically":[93],"re-":[94],"activate":[95],"healthiest":[97],"back":[99],"train":[101],"it":[102],"again":[103],"regain":[105],"accuracy":[106,139,170,201,205,227],"greatly":[108],"ameliorate":[109],"quality.":[113],"Based":[114],"experiments":[116],"MNIST":[118],"[3]":[119],"dataset,":[120],"our":[121,245],"method":[122],"compress":[124,195,220],"convolution":[125],"layers":[126,241],"VGG-like":[128],"[10]":[129],"up":[131,153],"60%":[133],"with":[134,167,181],"0.5%":[135],"increase":[136,168,203],"in":[137,169,204,250],"test":[138],"within":[140],"less":[141],"than":[142],"half":[144],"number":[146],"initial":[148],"amount":[149],"(speed-up":[152],"2.5\u00d7),":[155],"state-":[156],"of-the-art":[157],"results":[158],"dropping":[160,174],"80%":[161],"(compressed":[164,178],"86%":[165],"parameters)":[166,180],"0.14%.":[172],"Further":[173],"84%":[175],"94%":[179],"loss":[183],"0.4%":[185],"accuracy.":[186,232],"The":[187],"first":[188],"Auto-AEC":[190],"(Accuracy-Ensured":[191],"Compression)":[192],"network":[197],"preserving":[199,225],"original":[200,226],"or":[202,228],"whereas,":[209],"second":[211],"Auto-CECA":[213],"(Compression-Ensured":[214],"Considering":[215],"Accuracy)":[217],"maximum":[223],"minimal":[229],"drop":[230],"We":[233],"further":[234],"analyze":[235],"effectiveness":[236],"different":[240],"how":[244],"explores":[247],"exploits":[249],"various":[251],"stages":[252],"training.":[254]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2020-12-07T00:00:00"}