{"id":"https://openalex.org/W3034550700","doi":"https://doi.org/10.1109/globecom42002.2020.9322333","title":"A Framework for Neural Network Pruning Using Gibbs Distributions","display_name":"A Framework for Neural Network Pruning Using Gibbs Distributions","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3034550700","doi":"https://doi.org/10.1109/globecom42002.2020.9322333","mag":"3034550700"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2006.04981","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067139117","display_name":"Alex Labach","orcid":"https://orcid.org/0000-0002-4746-2718"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alex Labach","raw_affiliation_strings":["University of Toronto, Toronto, Canada","University of Toronto,,Department of Electrical and Computer Engineering,Toronto,,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto, Toronto, Canada","institution_ids":["https://openalex.org/I185261750"]},{"raw_affiliation_string":"University of Toronto,,Department of Electrical and Computer Engineering,Toronto,,Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089951737","display_name":"Shahrokh Valaee","orcid":"https://orcid.org/0000-0001-6254-1660"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Shahrokh Valaee","raw_affiliation_strings":["University of Toronto, Toronto, Canada","University of Toronto,,Department of Electrical and Computer Engineering,Toronto,,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto, Toronto, Canada","institution_ids":["https://openalex.org/I185261750"]},{"raw_affiliation_string":"University of Toronto,,Department of Electrical and Computer Engineering,Toronto,,Canada","institution_ids":["https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I185261750"],"apc_list":null,"apc_paid":null,"fwci":0.9781,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72894832,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9865000247955322,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.691573441028595},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6431237459182739},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.6345638632774353},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.512395441532135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48811420798301697},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4836541414260864},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.4475473165512085}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.691573441028595},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6431237459182739},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6345638632774353},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.512395441532135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48811420798301697},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4836541414260864},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.4475473165512085},{"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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322333","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2006.04981","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.04981","pdf_url":"https://arxiv.org/pdf/2006.04981","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3034550700","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2006.04981.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2006.04981","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2006.04981","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2006.04981","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.04981","pdf_url":"https://arxiv.org/pdf/2006.04981","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321879","display_name":"Fujitsu","ror":"https://ror.org/038e2g226"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W621546036","https://openalex.org/W1969758109","https://openalex.org/W2024060531","https://openalex.org/W2194775991","https://openalex.org/W2964233199","https://openalex.org/W6600213771","https://openalex.org/W6631190155","https://openalex.org/W6677580257","https://openalex.org/W6684191040","https://openalex.org/W6711642543","https://openalex.org/W6725543821","https://openalex.org/W6726275242","https://openalex.org/W6751979845","https://openalex.org/W6755843862","https://openalex.org/W6759263581","https://openalex.org/W6762269264","https://openalex.org/W6762947928","https://openalex.org/W6763726227","https://openalex.org/W6774302960","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W2963859455","https://openalex.org/W3088776583","https://openalex.org/W1583505058","https://openalex.org/W2805487899","https://openalex.org/W3023448965","https://openalex.org/W2437421599","https://openalex.org/W2890850650","https://openalex.org/W2962791537","https://openalex.org/W1501163499","https://openalex.org/W3041595026","https://openalex.org/W2963756295","https://openalex.org/W3007902335","https://openalex.org/W2022665301","https://openalex.org/W2549019841","https://openalex.org/W2964335397","https://openalex.org/W2610550445","https://openalex.org/W3176424395","https://openalex.org/W3026039950","https://openalex.org/W2989648302","https://openalex.org/W3194784700"],"abstract_inverted_index":{"Neural":[0],"network":[1,28,64],"pruning":[2,29,59,93],"is":[3],"an":[4,61],"important":[5],"technique":[6],"for":[7,72,83,104],"creating":[8],"efficient":[9],"machine":[10],"learning":[11],"models":[12],"that":[13,39,100],"can":[14,69],"run":[15],"on":[16,31,42,95],"edge":[17],"devices.":[18],"We":[19,34,85],"propose":[20],"a":[21],"new,":[22],"highly":[23],"flexible":[24],"approach":[25],"to":[26,52,57,90],"neural":[27,63],"based":[30,41],"Gibbs":[32,50],"distributions.":[33],"apply":[35],"it":[36,101],"with":[37],"Hamiltonians":[38],"are":[40],"weight":[43],"magnitude,":[44],"using":[45],"the":[46],"annealing":[47],"capabilities":[48],"of":[49],"distributions":[51],"smoothly":[53],"move":[54],"from":[55],"regularization":[56],"adaptive":[58],"during":[60],"ordinary":[62],"training":[65],"schedule.":[66],"This":[67],"method":[68,89],"be":[70],"used":[71],"either":[73],"unstructured":[74],"or":[75],"structured":[76],"pruning,":[77],"and":[78,98,107],"we":[79],"provide":[80],"explicit":[81],"formulations":[82],"both.":[84],"compare":[86],"our":[87],"proposed":[88],"several":[91],"established":[92],"methods":[94],"ResNet":[96],"variants":[97],"find":[99],"outperforms":[102],"them":[103],"unstructured,":[105],"kernel-wise,":[106],"filter-wise":[108],"pruning.":[109]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}