iBet uBet
web content aggregator. Adding the entire web to your favor.
Link to original content:
https://api.openalex.org/works/doi:10.1109/WACV51458.2022.00357
{"id":"https://openalex.org/W4213004369","doi":"https://doi.org/10.1109/wacv51458.2022.00357","title":"Channel Pruning via Lookahead Search Guided Reinforcement Learning","display_name":"Channel Pruning via Lookahead Search Guided Reinforcement Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4213004369","doi":"https://doi.org/10.1109/wacv51458.2022.00357"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv51458.2022.00357","pdf_url":null,"source":{"id":"https://openalex.org/S4363607979","display_name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5100338343","display_name":"Zi Wang","orcid":"https://orcid.org/0000-0003-3197-2550"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zi Wang","raw_affiliation_strings":["University of Tennessee, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100324623","display_name":"Chengcheng Li","orcid":"https://orcid.org/0000-0003-3507-8935"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengcheng Li","raw_affiliation_strings":["University of Tennessee, Knoxville, TN, USA"],"affiliations":[{"raw_affiliation_string":"University of Tennessee, Knoxville, TN, USA","institution_ids":["https://openalex.org/I75027704"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.716,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.999873,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":95},"biblio":{"volume":"529","issue":null,"first_page":"3513","last_page":"3524"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Deep Learning in Computer Vision and Image Recognition","score":0.9944,"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":"Deep Learning in Computer Vision and Image Recognition","score":0.9944,"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/T12611","display_name":"Photonic Reservoir Computing for Neural Computation","score":0.9902,"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":"Advances in Transfer Learning and Domain Adaptation","score":0.9894,"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/pruning","display_name":"Pruning","score":0.8149978},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.7055348},{"id":"https://openalex.org/keywords/neural-network-training","display_name":"Neural Network Training","score":0.566212},{"id":"https://openalex.org/keywords/representation-learning","display_name":"Representation Learning","score":0.558199},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5456685},{"id":"https://openalex.org/keywords/visual-recognition","display_name":"Visual Recognition","score":0.516013},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer Vision","score":0.514009},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-Supervised Learning","score":0.512545}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8149978},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77300906},{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.7213197},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.716743},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7055348},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.59454733},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5831111},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5456685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.51940465},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5022464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48400185},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4802949},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45055345},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.22632027},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15833008},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv51458.2022.00357","pdf_url":null,"source":{"id":"https://openalex.org/S4363607979","display_name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":94,"referenced_works":["https://openalex.org/W1191599655","https://openalex.org/W1625390266","https://openalex.org/W1686810756","https://openalex.org/W1714211023","https://openalex.org/W1821462560","https://openalex.org/W1903029394","https://openalex.org/W1989883122","https://openalex.org/W2050604187","https://openalex.org/W2065339974","https://openalex.org/W2097117768","https://openalex.org/W2102256448","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2119144962","https://openalex.org/W2123469553","https://openalex.org/W2126316555","https://openalex.org/W2145339207","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2216125271","https://openalex.org/W2257979135","https://openalex.org/W2300242332","https://openalex.org/W2495425901","https://openalex.org/W2553303224","https://openalex.org/W2563705555","https://openalex.org/W2591344039","https://openalex.org/W2707890836","https://openalex.org/W2739066022","https://openalex.org/W2750784772","https://openalex.org/W2754084392","https://openalex.org/W2756085244","https://openalex.org/W2766447205","https://openalex.org/W2784097471","https://openalex.org/W2786622092","https://openalex.org/W2808166015","https://openalex.org/W2808168148","https://openalex.org/W2883780447","https://openalex.org/W2896409484","https://openalex.org/W2905741102","https://openalex.org/W2907599120","https://openalex.org/W2924515500","https://openalex.org/W2928560789","https://openalex.org/W2945403477","https://openalex.org/W2949941638","https://openalex.org/W2950656546","https://openalex.org/W2952164265","https://openalex.org/W2962861284","https://openalex.org/W2962965870","https://openalex.org/W2962988160","https://openalex.org/W2963000224","https://openalex.org/W2963037989","https://openalex.org/W2963122961","https://openalex.org/W2963140066","https://openalex.org/W2963363373","https://openalex.org/W2963382930","https://openalex.org/W2963416938","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2963518130","https://openalex.org/W2963684088","https://openalex.org/W2963864421","https://openalex.org/W2964019666","https://openalex.org/W2964043796","https://openalex.org/W2964217848","https://openalex.org/W2964233199","https://openalex.org/W2964255836","https://openalex.org/W2969958526","https://openalex.org/W2974893078","https://openalex.org/W2984618279","https://openalex.org/W3028304412","https://openalex.org/W3034251466","https://openalex.org/W3034411059","https://openalex.org/W3034513523","https://openalex.org/W3034818206","https://openalex.org/W3035442140","https://openalex.org/W3035467254","https://openalex.org/W3083720136","https://openalex.org/W3100859887","https://openalex.org/W3104263540","https://openalex.org/W3107407793","https://openalex.org/W3118608800","https://openalex.org/W3119516214","https://openalex.org/W3132095740","https://openalex.org/W3169059075","https://openalex.org/W3175185622","https://openalex.org/W3181161645","https://openalex.org/W4286984660","https://openalex.org/W4287998573","https://openalex.org/W4295185264","https://openalex.org/W4298857966","https://openalex.org/W4300081896","https://openalex.org/W4300485340","https://openalex.org/W4320013936","https://openalex.org/W639708223"],"related_works":["https://openalex.org/W992687842","https://openalex.org/W4386603768","https://openalex.org/W4300560302","https://openalex.org/W4285312748","https://openalex.org/W3203767529","https://openalex.org/W3136325136","https://openalex.org/W3088108839","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547"],"abstract_inverted_index":{"Channel":[0],"pruning":[1,73,155],"has":[2],"become":[3],"an":[4],"effective":[5],"yet":[6],"still":[7],"challenging":[8],"approach":[9,65,146],"to":[10,17,36,51,66,94,118,148],"achieve":[11],"compact":[12],"neural":[13,82],"networks.":[14],"It":[15],"aims":[16],"prune":[18,95],"the":[19,32,37,53,68,96,103,106,128,132,142],"optimal":[20,97],"set":[21],"of":[22,31,42,99,105,144],"filters":[23,100],"whose":[24],"removal":[25],"results":[26],"in":[27,71],"minimal":[28],"performance":[29,104],"degradation":[30],"slimmed":[33],"network.":[34,108],"Due":[35],"prohibitively":[38],"vast":[39],"search":[40,76,116,122],"space":[41],"filter":[43,54,69,124],"combinations,":[44],"existing":[45,153],"approaches":[46],"usually":[47],"use":[48],"various":[49],"criteria":[50],"estimate":[52],"importance":[55],"while":[56],"sacrificing":[57],"some":[58],"precision.":[59],"Here":[60],"we":[61,111],"present":[62],"a":[63,120],"new":[64],"optimizing":[67],"selection":[70],"channel":[72,154],"with":[74,92],"lookahead":[75,121],"guided":[77],"reinforcement":[78],"learning":[79],"(RL).":[80],"A":[81],"network":[83],"that":[84],"takes":[85],"as":[86],"input":[87],"filterrelated":[88],"features":[89],"is":[90],"trained":[91],"RL":[93,133],"sequence":[98],"and":[101,139,151],"maximize":[102],"remaining":[107],"In":[109],"addition,":[110],"employ":[112],"Monte":[113],"Carlo":[114],"tree":[115],"(MCTS)":[117],"provide":[119],"for":[123,131],"selection,":[125],"which":[126],"increases":[127],"sample":[129],"efficiency":[130],"training.":[134],"Experiments":[135],"on":[136],"MNIST,":[137],"CIFAR-10,":[138],"ILSVRC-2012":[140],"validate":[141],"effectiveness":[143],"our":[145],"compared":[147],"both":[149],"traditional":[150],"automated":[152],"approaches.":[156]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4213004369","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2024-10-22T17:07:27.189321","created_date":"2022-02-24"}
