{"id":"https://openalex.org/W2991065888","doi":"https://doi.org/10.1109/hpec.2019.8916385","title":"Training Behavior of Sparse Neural Network Topologies","display_name":"Training Behavior of Sparse Neural Network Topologies","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2991065888","doi":"https://doi.org/10.1109/hpec.2019.8916385","mag":"2991065888"},"language":"en","primary_location":{"id":"doi:10.1109/hpec.2019.8916385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2019.8916385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.00299","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Simon Alford","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Simon Alford","raw_affiliation_strings":["Mathematics Department, MIT"],"affiliations":[{"raw_affiliation_string":"Mathematics Department, MIT","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ryan Robinett","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ryan Robinett","raw_affiliation_strings":["Mathematics Department, MIT"],"affiliations":[{"raw_affiliation_string":"Mathematics Department, MIT","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lauren Milechin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lauren Milechin","raw_affiliation_strings":["MIT Department of Earth, Atmospheric, and Planetary Sciences"],"affiliations":[{"raw_affiliation_string":"MIT Department of Earth, Atmospheric, and Planetary Sciences","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Jeremy Kepner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jeremy Kepner","raw_affiliation_strings":["Mathematics Department, MIT"],"affiliations":[{"raw_affiliation_string":"Mathematics Department, MIT","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4087,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.66706189,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/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/T12676","display_name":"Machine Learning and ELM","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/T10320","display_name":"Neural Networks and Applications","score":0.9991999864578247,"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/network-topology","display_name":"Network topology","score":0.7703999876976013},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7073000073432922},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.6402999758720398},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.5863999724388123},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.43149998784065247},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.424699991941452},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.36890000104904175}],"concepts":[{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.7703999876976013},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7073000073432922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6912000179290771},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6402999758720398},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.5863999724388123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5841000080108643},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44690001010894775},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.43149998784065247},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.424699991941452},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.36890000104904175},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.350600004196167},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.32600000500679016},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2775999903678894},{"id":"https://openalex.org/C177973122","wikidata":"https://www.wikidata.org/wiki/Q7860946","display_name":"Types of artificial neural networks","level":4,"score":0.26420000195503235},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/hpec.2019.8916385","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec.2019.8916385","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1810.00299","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.00299","pdf_url":"https://arxiv.org/pdf/1810.00299","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1810.00299","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.00299","pdf_url":"https://arxiv.org/pdf/1810.00299","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W26556108","https://openalex.org/W2058641082","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2147270524","https://openalex.org/W2194775991","https://openalex.org/W2743799610","https://openalex.org/W2804905035","https://openalex.org/W2809429607","https://openalex.org/W4389158502","https://openalex.org/W6637373629","https://openalex.org/W6677103964","https://openalex.org/W6683826617","https://openalex.org/W6684191040","https://openalex.org/W6724998850","https://openalex.org/W6732814185","https://openalex.org/W6736780897","https://openalex.org/W6745148473","https://openalex.org/W6745849810"],"related_works":[],"abstract_inverted_index":{"Improvements":[0],"in":[1,30,119],"the":[2,12,44],"performance":[3],"of":[4,14,46,92,115],"deep":[5],"neural":[6,48,67],"networks":[7,105],"have":[8],"often":[9],"come":[10],"through":[11],"design":[13,45],"larger":[15],"and":[16,56,98],"more":[17],"complex":[18],"networks.":[19],"As":[20],"a":[21,26,90],"result,":[22],"fast":[23],"memory":[24],"is":[25,43],"significant":[27],"limiting":[28],"factor":[29],"our":[31],"ability":[32],"to":[33,39,109],"improve":[34],"network":[35,68,81,93],"performance.":[36],"One":[37],"approach":[38],"overcoming":[40],"this":[41,60],"limit":[42],"sparse":[47,66,104],"networks,":[49,111],"which":[50,74,121],"can":[51],"be":[52],"both":[53],"very":[54],"large":[55],"efficiently":[57],"trained.":[58],"In":[59],"paper":[61],"we":[62],"experiment":[63],"training":[64],"on":[65],"topologies.":[69],"We":[70],"test":[71],"pruning-based":[72],"topologies,":[73],"are":[75,84],"derived":[76],"from":[77],"an":[78],"initially":[79],"dense":[80,110],"whose":[82],"connections":[83],"pruned,":[85],"as":[86,88],"well":[87],"RadiX-Nets,":[89],"class":[91],"topologies":[94],"with":[95],"proven":[96],"connectivity":[97],"sparsity":[99,116],"properties.":[100],"Results":[101],"show":[102],"that":[103],"obtain":[106],"accuracies":[107],"comparable":[108],"but":[112],"extreme":[113],"levels":[114],"cause":[117],"instability":[118],"training,":[120],"merits":[122],"further":[123],"study.":[124]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2019-12-05T00:00:00"}
