{"id":"https://openalex.org/W3185365640","doi":"https://doi.org/10.1109/jstars.2021.3096195","title":"Toward Near-Real-Time Training With Semi-Random Deep Neural Networks and Tensor-Train Decomposition","display_name":"Toward Near-Real-Time Training With Semi-Random Deep Neural Networks and Tensor-Train Decomposition","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3185365640","doi":"https://doi.org/10.1109/jstars.2021.3096195","mag":"3185365640"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2021.3096195","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3096195","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09492908.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09492908.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055321869","display_name":"Humza Syed","orcid":"https://orcid.org/0000-0001-5897-4471"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Humza Syed","raw_affiliation_strings":["Neuromorphic AI Lab, Rochester Institute of Technology, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Neuromorphic AI Lab, Rochester Institute of Technology, Rochester, NY, USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044803130","display_name":"Ryan Bryla","orcid":null},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ryan Bryla","raw_affiliation_strings":["Neuromorphic AI Lab, Rochester Institute of Technology, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Neuromorphic AI Lab, Rochester Institute of Technology, Rochester, NY, USA","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102874846","display_name":"Uttam Majumder","orcid":"https://orcid.org/0000-0002-5244-2500"},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Uttam Majumder","raw_affiliation_strings":["Air Force Research Lab, Rome, NY, USA"],"affiliations":[{"raw_affiliation_string":"Air Force Research Lab, Rome, NY, USA","institution_ids":["https://openalex.org/I1280414376"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067236813","display_name":"Dhireesha Kudithipudi","orcid":"https://orcid.org/0000-0003-4462-5224"},"institutions":[{"id":"https://openalex.org/I45438204","display_name":"The University of Texas at San Antonio","ror":"https://ror.org/01kd65564","country_code":"US","type":"education","lineage":["https://openalex.org/I45438204"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhireesha Kudithipudi","raw_affiliation_strings":["Neuromorphic AI Lab, University of Texas at San Antonio, San Antonio, TX, USA"],"affiliations":[{"raw_affiliation_string":"Neuromorphic AI Lab, University of Texas at San Antonio, San Antonio, TX, USA","institution_ids":["https://openalex.org/I45438204"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055321869"],"corresponding_institution_ids":["https://openalex.org/I155173764"],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09794438,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"8171","last_page":"8179"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983999729156494,"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":0.9929999709129333,"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/computer-science","display_name":"Computer science","score":0.7746153473854065},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.6974912285804749},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.608162522315979},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5361774563789368},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5252903699874878},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5226476192474365},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.506864607334137},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.49935483932495117},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4323875308036804},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42888593673706055},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3290855288505554},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32066091895103455},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09262341260910034}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7746153473854065},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.6974912285804749},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.608162522315979},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5361774563789368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5252903699874878},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5226476192474365},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.506864607334137},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.49935483932495117},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4323875308036804},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42888593673706055},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3290855288505554},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32066091895103455},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09262341260910034},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2021.3096195","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3096195","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09492908.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:49ea8e61c76549cd91ae752fd6261e0c","is_oa":true,"landing_page_url":"https://doaj.org/article/49ea8e61c76549cd91ae752fd6261e0c","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8171-8179 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2021.3096195","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2021.3096195","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/9314330/09492908.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.44999998807907104,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3185365640.pdf","grobid_xml":"https://content.openalex.org/works/W3185365640.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W78356000","https://openalex.org/W1485009520","https://openalex.org/W1498436455","https://openalex.org/W1580963329","https://openalex.org/W1588594383","https://openalex.org/W1798945469","https://openalex.org/W1965895201","https://openalex.org/W1974511160","https://openalex.org/W1981745143","https://openalex.org/W1993482030","https://openalex.org/W2011301426","https://openalex.org/W2012638612","https://openalex.org/W2024165284","https://openalex.org/W2034344316","https://openalex.org/W2108598243","https://openalex.org/W2119144962","https://openalex.org/W2120475512","https://openalex.org/W2134557905","https://openalex.org/W2134603844","https://openalex.org/W2138383519","https://openalex.org/W2142947774","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2258054274","https://openalex.org/W2290355700","https://openalex.org/W2329684222","https://openalex.org/W2514029627","https://openalex.org/W2516100594","https://openalex.org/W2546302380","https://openalex.org/W2612445135","https://openalex.org/W2750384547","https://openalex.org/W2752782242","https://openalex.org/W2754586266","https://openalex.org/W2788388592","https://openalex.org/W2798989280","https://openalex.org/W2885420314","https://openalex.org/W2899771611","https://openalex.org/W2946813660","https://openalex.org/W2962747323","https://openalex.org/W2962899986","https://openalex.org/W2963125010","https://openalex.org/W2963381188","https://openalex.org/W2963420686","https://openalex.org/W2963446712","https://openalex.org/W2964299589","https://openalex.org/W2966695781","https://openalex.org/W3102368068","https://openalex.org/W3120740533","https://openalex.org/W4297775537","https://openalex.org/W6603161775","https://openalex.org/W6677580257","https://openalex.org/W6679935922","https://openalex.org/W6684191040","https://openalex.org/W6737664043","https://openalex.org/W6743688258","https://openalex.org/W6747722507","https://openalex.org/W6756040250"],"related_works":["https://openalex.org/W2064168458","https://openalex.org/W4287750422","https://openalex.org/W2592293938","https://openalex.org/W3197970974","https://openalex.org/W4385436225","https://openalex.org/W2033523051","https://openalex.org/W2794358477","https://openalex.org/W2981022037","https://openalex.org/W4324290738","https://openalex.org/W2751890684"],"abstract_inverted_index":{"In":[0,35],"recent":[1],"years,":[2],"deep":[3,41],"neural":[4,42],"networks":[5,20,43,76,151],"have":[6],"shown":[7,72],"to":[8,44,103,136],"achieve":[9,45],"state-of-the-art":[10],"performance":[11,90],"on":[12,67],"several":[13],"classification":[14],"and":[15,49,81,145],"prediction":[16],"tasks.":[17],"However,":[18],"these":[19,110],"demand":[21],"undesirable":[22],"lengthy":[23],"training":[24,48,147],"times":[25],"coupled":[26],"with":[27,77],"high":[28],"computational":[29,51],"resources":[30],"(memory,":[31],"I/O,":[32],"processing":[33],"time).":[34],"this":[36,64],"work,":[37],"we":[38],"explore":[39],"semi-random":[40],"near":[46],"real-time":[47,62,94],"less":[50],"resource":[52],"usage.":[53],"Although":[54],"many":[55],"works":[56],"enhance":[57],"the":[58,87,106,115,121,130],"underlying":[59],"hardware":[60],"for":[61,93],"training,":[63],"work":[65],"focuses":[66],"algorithmic":[68],"optimization.":[69],"It":[70],"is":[71,101],"that":[73],"random":[74,124,149],"projection":[75,125,150],"additional":[78],"skipped":[79],"connectivity":[80],"randomly":[82],"weighted":[83],"layers":[84],"can":[85,152],"boost":[86],"overall":[88],"network":[89],"while":[91],"enabling":[92],"training.":[95],"Additionally,":[96],"a":[97,137],"tensor-train":[98],"decomposition":[99,119],"technique":[100],"leveraged":[102],"further":[104],"reduce":[105],"model":[107],"complexity":[108,122],"of":[109,123,129],"networks.":[111],"Our":[112],"investigation":[113],"accomplishes":[114],"following:":[116],"1)":[117],"Tensor-train":[118],"decreases":[120],"networks,":[126],"2)":[127],"compression":[128],"fully":[131],"connected":[132],"hidden":[133],"layer":[134],"leads":[135],"minimum":[138],"~":[139],"40\u00d7":[140],"decrease":[141],"in":[142,155],"memory":[143],"size,":[144],"3)":[146],"under":[148],"be":[153],"achieved":[154],"near-real":[156],"time.":[157]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
