{"id":"https://openalex.org/W2978213262","doi":"https://doi.org/10.1109/ijcnn.2019.8851903","title":"Sparsity as the Implicit Gating Mechanism for Residual Blocks","display_name":"Sparsity as the Implicit Gating Mechanism for Residual Blocks","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978213262","doi":"https://doi.org/10.1109/ijcnn.2019.8851903","mag":"2978213262"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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":"https://openalex.org/A5028569628","display_name":"Shaeke Salman","orcid":null},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shaeke Salman","raw_affiliation_strings":["Department of Computer Science, Florida State University, Tallahassee, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102867647","display_name":"Xiuwen Liu","orcid":"https://orcid.org/0000-0002-9320-3872"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiuwen Liu","raw_affiliation_strings":["Department of Computer Science, Florida State University, Tallahassee, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028569628"],"corresponding_institution_ids":["https://openalex.org/I103163165"],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.54304837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"abs 1505 387","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9997000098228455,"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/residual","display_name":"Residual","score":0.9069058895111084},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8016801476478577},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.6450155377388},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6038782000541687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.571005642414093},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5196781754493713},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5019595623016357},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.44147759675979614},{"id":"https://openalex.org/keywords/gating","display_name":"Gating","score":0.4225034713745117},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36154240369796753},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33265817165374756}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.9069058895111084},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8016801476478577},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.6450155377388},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6038782000541687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.571005642414093},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5196781754493713},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5019595623016357},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.44147759675979614},{"id":"https://openalex.org/C194544171","wikidata":"https://www.wikidata.org/wiki/Q21105679","display_name":"Gating","level":2,"score":0.4225034713745117},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36154240369796753},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33265817165374756},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C42407357","wikidata":"https://www.wikidata.org/wiki/Q521","display_name":"Physiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1026270304","https://openalex.org/W1686810756","https://openalex.org/W1850240193","https://openalex.org/W2117539524","https://openalex.org/W2130942839","https://openalex.org/W2143612262","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2399994860","https://openalex.org/W2401231614","https://openalex.org/W2541674938","https://openalex.org/W2549139847","https://openalex.org/W2561907692","https://openalex.org/W2565538933","https://openalex.org/W2766447205","https://openalex.org/W2950621961","https://openalex.org/W2952574409","https://openalex.org/W2962835968","https://openalex.org/W2963410064","https://openalex.org/W2963586744","https://openalex.org/W2964137095","https://openalex.org/W2964160102","https://openalex.org/W3118608800","https://openalex.org/W4297688150","https://openalex.org/W6626481562","https://openalex.org/W6637373629","https://openalex.org/W6638926925","https://openalex.org/W6677651945","https://openalex.org/W6679436768","https://openalex.org/W6681804681","https://openalex.org/W6684191040","https://openalex.org/W6698183232","https://openalex.org/W6712446393","https://openalex.org/W6713132643","https://openalex.org/W6729059855","https://openalex.org/W6729342207","https://openalex.org/W6730741096","https://openalex.org/W6731278065","https://openalex.org/W6780493881","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W3196952692","https://openalex.org/W2984708981","https://openalex.org/W4300939921","https://openalex.org/W2964350391","https://openalex.org/W2274287116","https://openalex.org/W2897517148","https://openalex.org/W3046945067","https://openalex.org/W2983358626","https://openalex.org/W3160076723","https://openalex.org/W2967403871"],"abstract_inverted_index":{"Neural":[0],"networks":[1,44,63,74],"are":[2,48,163],"the":[3,7,86,91,110,113,119,124,134,146,171,189,202],"core":[4],"component":[5],"in":[6,15,25,28,105],"recent":[8],"empirical":[9],"successes":[10],"of":[11,42,60,85,130,148,192],"deep":[12,150,206],"learning":[13],"techniques":[14],"challenging":[16],"tasks.":[17],"Residual":[18],"network":[19],"(ResNet)":[20],"architectures":[21],"have":[22],"been":[23],"instrumental":[24],"improving":[26],"performance":[27],"object":[29],"recognition":[30],"and":[31,66,196],"other":[32],"tasks":[33],"by":[34],"enabling":[35],"training":[36,149],"much":[37],"deeper":[38],"neural":[39,151],"networks.":[40,152,208],"Studies":[41],"residual":[43,62,87,175,190,207],"reveal":[45],"that":[46,83,140,162,198],"they":[47,68],"robust":[49],"to":[50,72,116,118,143,145,165],"removing":[51],"layers.":[52,77],"However,":[53],"it":[54,70,100,132,158],"is":[55,98,141,156],"still":[56],"an":[57,106],"open":[58],"question":[59],"why":[61],"behave":[64],"well":[65],"how":[67],"make":[69],"feasible":[71],"train":[73],"with":[75],"many":[76],"In":[78],"this":[79],"paper,":[80],"we":[81],"show":[82,197],"sparsity":[84,199],"blocks":[88,176,191],"acts":[89,200],"as":[90,102,201],"implicit":[92,203],"gating":[93],"mechanism.":[94],"When":[95,153],"a":[96,103,128,154],"neuron":[97,155],"inactive,":[99],"behaves":[101],"node":[104],"information":[107,111],"highway,":[108],"allowing":[109],"from":[112],"previous":[114],"layer":[115,121],"pass":[117],"next":[120],"unchanged.":[122],"As":[123],"identity":[125],"function":[126],"has":[127],"derivative":[129],"1,":[131],"avoids":[133],"exploding":[135],"or":[136],"vanishing":[137],"gradient":[138],"problem":[139],"known":[142],"contribute":[144],"difficulty":[147],"active,":[157],"captures":[159],"input-output":[160],"relationships":[161],"necessary":[164],"achieve":[166],"good":[167],"performance.":[168],"By":[169],"using":[170],"ReLu":[172],"activation":[173],"functions,":[174],"produce":[177],"sparse":[178],"outputs":[179],"for":[180,205],"typical":[181],"inputs.":[182],"We":[183],"perform":[184],"systematic":[185],"experimental":[186],"analysis":[187],"on":[188],"trained":[193],"ResNet":[194],"models":[195],"gate":[204]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
