{"id":"https://openalex.org/W2963319203","doi":"https://doi.org/10.1109/tc.2019.2914438","title":"NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm","display_name":"NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm","publication_year":2019,"publication_date":"2019-05-02","ids":{"openalex":"https://openalex.org/W2963319203","doi":"https://doi.org/10.1109/tc.2019.2914438","mag":"2963319203"},"language":"en","primary_location":{"id":"doi:10.1109/tc.2019.2914438","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tc.2019.2914438","pdf_url":null,"source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","0016-9340","1557-9956","2326-3814"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computers","raw_type":"journal-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/A5080137972","display_name":"Xiaoliang Dai","orcid":"https://orcid.org/0000-0003-3098-2714"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoliang Dai","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0003-3098-2714","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002444694","display_name":"Hongxu Yin","orcid":"https://orcid.org/0000-0002-6481-6389"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongxu Yin","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-6481-6389","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086131079","display_name":"Niraj K. Jha","orcid":"https://orcid.org/0000-0002-1539-0369"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Niraj K. Jha","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-1539-0369","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":15.0865,"has_fulltext":false,"cited_by_count":216,"citation_normalized_percentile":{"value":0.99174172,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"68","issue":"10","first_page":"1487","last_page":"1497"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9975000023841858,"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/T12535","display_name":"Machine Learning and Data Classification","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/flops","display_name":"FLOPS","score":0.9240615367889404},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7704964876174927},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7427252531051636},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.5791270732879639},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5683004856109619},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5226916074752808},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.480782687664032},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46349072456359863},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.4544752240180969},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4269316494464874},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4114904999732971},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3239400088787079},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.30057334899902344},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1167336106300354},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09199810028076172},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.06558889150619507}],"concepts":[{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.9240615367889404},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7704964876174927},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7427252531051636},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.5791270732879639},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5683004856109619},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5226916074752808},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.480782687664032},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46349072456359863},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.4544752240180969},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4269316494464874},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4114904999732971},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3239400088787079},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.30057334899902344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1167336106300354},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09199810028076172},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.06558889150619507},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tc.2019.2914438","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tc.2019.2914438","pdf_url":null,"source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","0016-9340","1557-9956","2326-3814"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W992687842","https://openalex.org/W1498436455","https://openalex.org/W1570197553","https://openalex.org/W1598866093","https://openalex.org/W1686810756","https://openalex.org/W1724438581","https://openalex.org/W1836465849","https://openalex.org/W1921523184","https://openalex.org/W1935978687","https://openalex.org/W1963324709","https://openalex.org/W1981268965","https://openalex.org/W2042481239","https://openalex.org/W2097117768","https://openalex.org/W2104867159","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2152332944","https://openalex.org/W2155893237","https://openalex.org/W2163605009","https://openalex.org/W2167215970","https://openalex.org/W2194775991","https://openalex.org/W2271840356","https://openalex.org/W2285660444","https://openalex.org/W2495425901","https://openalex.org/W2509836288","https://openalex.org/W2540366045","https://openalex.org/W2553303224","https://openalex.org/W2556833785","https://openalex.org/W2593744649","https://openalex.org/W2594529350","https://openalex.org/W2610817424","https://openalex.org/W2619096655","https://openalex.org/W2899771611","https://openalex.org/W2949264490","https://openalex.org/W2950248853","https://openalex.org/W2962835968","https://openalex.org/W2962965870","https://openalex.org/W2963000224","https://openalex.org/W2963374479","https://openalex.org/W2963452728","https://openalex.org/W2963674932","https://openalex.org/W2963981420","https://openalex.org/W2964081807","https://openalex.org/W2964217848","https://openalex.org/W2964233199","https://openalex.org/W4293406386","https://openalex.org/W4295185264","https://openalex.org/W4300485543","https://openalex.org/W6629815555","https://openalex.org/W6634294911","https://openalex.org/W6635810480","https://openalex.org/W6637373629","https://openalex.org/W6637709462","https://openalex.org/W6638667902","https://openalex.org/W6640185926","https://openalex.org/W6640289440","https://openalex.org/W6645729982","https://openalex.org/W6675791501","https://openalex.org/W6684191040","https://openalex.org/W6684563725","https://openalex.org/W6694517276","https://openalex.org/W6723181079","https://openalex.org/W6724998850","https://openalex.org/W6725543821","https://openalex.org/W6725588366","https://openalex.org/W6726275242","https://openalex.org/W6729092785","https://openalex.org/W6729956949","https://openalex.org/W6729972426","https://openalex.org/W6734593296","https://openalex.org/W6737283639","https://openalex.org/W6738642365","https://openalex.org/W6738957339","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W3131497135","https://openalex.org/W3160421061","https://openalex.org/W4200040586","https://openalex.org/W2768083806","https://openalex.org/W3139123974","https://openalex.org/W4287364200","https://openalex.org/W3006772089","https://openalex.org/W2964233199","https://openalex.org/W3129164450","https://openalex.org/W2788653909"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNNs)":[3],"have":[4,7],"begun":[5],"to":[6,59,80,187],"a":[8,51,71,92,129],"pervasive":[9],"impact":[10],"on":[11],"various":[12],"applications":[13,28],"of":[14,20,84,112,132],"machine":[15],"learning.":[16],"However,":[17],"the":[18,82,98,104,137,156],"problem":[19],"finding":[21],"an":[22],"optimal":[23],"DNN":[24,38,65,72],"architecture":[25,105,134],"for":[26,34],"large":[27],"is":[29],"challenging.":[30],"Common":[31],"approaches":[32],"go":[33],"deeper":[35],"and":[36,63,86,109,114,148,159,172,183],"larger":[37],"architectures":[39,66],"but":[40],"may":[41],"incur":[42],"substantial":[43],"redundancy.":[44],"To":[45],"address":[46],"these":[47],"problems,":[48],"we":[49,141,162],"introduce":[50],"network":[52,57,96,143,164],"growth":[53,108],"algorithm":[54],"that":[55,76,120],"complements":[56],"pruning":[58,111],"learn":[60],"both":[61,78],"weights":[62],"compact":[64,85,126],"during":[67],"training.":[68],"We":[69],"propose":[70],"synthesis":[73],"tool":[74],"(NeST)":[75],"combines":[77],"methods":[79],"automate":[81],"generation":[83],"accurate":[87],"DNNs.":[88],"NeST":[89,121],"starts":[90],"with":[91,106,128],"randomly":[93],"initialized":[94],"sparse":[95],"called":[97],"seed":[99,133],"architecture.":[100],"It":[101],"iteratively":[102],"tunes":[103],"gradient-based":[107],"magnitude-based":[110],"neurons":[113],"connections.":[115],"Our":[116],"experimental":[117],"results":[118],"show":[119],"yields":[122],"accurate,":[123],"yet":[124],"very":[125],"DNNs,":[127],"wide":[130],"range":[131],"selection.":[135],"For":[136,155],"LeNet-300-100":[138],"(LeNet-5)":[139],"architecture,":[140],"reduce":[142,163],"parameters":[144,165],"by":[145,152,167],"70.2\u00d7":[146],"(74.3\u00d7)":[147],"floating-point":[149],"operations":[150],"(FLOPs)":[151,166],"79.4\u00d7":[153],"(43.7\u00d7).":[154],"AlexNet,":[157],"VGG-16,":[158],"ResNet-50":[160],"architectures,":[161],"15.7\u00d7":[168],"(4.6\u00d7),":[169],"33.2\u00d7":[170],"(8.9\u00d7),":[171],"4.1\u00d7":[173],"(2.1\u00d7)":[174],"respectively.":[175],"NeST's":[176],"grow-and-prune":[177],"paradigm":[178],"delivers":[179],"significant":[180],"additional":[181],"parameter":[182],"FLOPs":[184],"reduction":[185],"relative":[186],"pruning-only":[188],"methods.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":37},{"year":2021,"cited_by_count":52},{"year":2020,"cited_by_count":43},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
