{"id":"https://openalex.org/W3044327318","doi":"https://doi.org/10.1109/tnnls.2020.3008154","title":"A Hybrid CMOS-Memristive Approach to Designing Deep Generative Models","display_name":"A Hybrid CMOS-Memristive Approach to Designing Deep Generative Models","publication_year":2020,"publication_date":"2020-07-23","ids":{"openalex":"https://openalex.org/W3044327318","doi":"https://doi.org/10.1109/tnnls.2020.3008154","mag":"3044327318","pmid":"https://pubmed.ncbi.nlm.nih.gov/32701452"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2020.3008154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3008154","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5066176656","display_name":"Vivek Parmar","orcid":"https://orcid.org/0000-0001-7380-0816"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vivek Parmar","raw_affiliation_strings":["IIT Delhi, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"IIT Delhi, New Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008011360","display_name":"Manan Suri","orcid":"https://orcid.org/0000-0003-1417-3570"},"institutions":[{"id":"https://openalex.org/I68891433","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06","country_code":"IN","type":"education","lineage":["https://openalex.org/I68891433"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manan Suri","raw_affiliation_strings":["IIT Delhi, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"IIT Delhi, New Delhi, India","institution_ids":["https://openalex.org/I68891433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066176656"],"corresponding_institution_ids":["https://openalex.org/I68891433"],"apc_list":null,"apc_paid":null,"fwci":0.3082,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.56402147,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"32","issue":"6","first_page":"2790","last_page":"2796"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.814559817314148},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.7320928573608398},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6927400231361389},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6829079389572144},{"id":"https://openalex.org/keywords/boltzmann-machine","display_name":"Boltzmann machine","score":0.6722316741943359},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6690912246704102},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6445004940032959},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5029768347740173},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5006470680236816},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.43149083852767944},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3926604390144348},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3470463752746582},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3436643183231354},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.2620207965373993}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.814559817314148},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.7320928573608398},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6927400231361389},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6829079389572144},{"id":"https://openalex.org/C192576344","wikidata":"https://www.wikidata.org/wiki/Q194706","display_name":"Boltzmann machine","level":3,"score":0.6722316741943359},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6690912246704102},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6445004940032959},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5029768347740173},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5006470680236816},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.43149083852767944},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3926604390144348},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3470463752746582},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3436643183231354},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2620207965373993},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2020.3008154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3008154","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:32701452","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32701452","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6200000047683716}],"awards":[{"id":"https://openalex.org/G2665857355","display_name":null,"funder_award_id":"CRG/2018/001901","funder_id":"https://openalex.org/F4320334771","funder_display_name":"Science and Engineering Research Board"}],"funders":[{"id":"https://openalex.org/F4320324473","display_name":"Indian Institute of Technology Delhi","ror":"https://ror.org/049tgcd06"},{"id":"https://openalex.org/F4320334771","display_name":"Science and Engineering Research Board","ror":"https://ror.org/03ffdsr55"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W44815768","https://openalex.org/W1596035946","https://openalex.org/W1937359183","https://openalex.org/W1972477204","https://openalex.org/W1973445088","https://openalex.org/W1991059746","https://openalex.org/W2004823737","https://openalex.org/W2018774711","https://openalex.org/W2030671441","https://openalex.org/W2060694887","https://openalex.org/W2072128103","https://openalex.org/W2077586448","https://openalex.org/W2077751791","https://openalex.org/W2092462444","https://openalex.org/W2100495367","https://openalex.org/W2102409316","https://openalex.org/W2110798204","https://openalex.org/W2113038496","https://openalex.org/W2120432001","https://openalex.org/W2122221396","https://openalex.org/W2136922672","https://openalex.org/W2139427956","https://openalex.org/W2145094598","https://openalex.org/W2147010501","https://openalex.org/W2155377787","https://openalex.org/W2155954834","https://openalex.org/W2158164339","https://openalex.org/W2344162344","https://openalex.org/W2398563917","https://openalex.org/W2503374103","https://openalex.org/W2526812109","https://openalex.org/W2584311717","https://openalex.org/W2727914278","https://openalex.org/W2767048768","https://openalex.org/W2783393355","https://openalex.org/W2783915901","https://openalex.org/W2914410297","https://openalex.org/W2997574889","https://openalex.org/W3005885031","https://openalex.org/W3104135012","https://openalex.org/W4231109964","https://openalex.org/W4236126228","https://openalex.org/W6648322874","https://openalex.org/W6675401909","https://openalex.org/W6676481782","https://openalex.org/W6677208260","https://openalex.org/W6681096077","https://openalex.org/W6681934674","https://openalex.org/W6683128514"],"related_works":["https://openalex.org/W2128151361","https://openalex.org/W2999408031","https://openalex.org/W2902142523","https://openalex.org/W2064630666","https://openalex.org/W2287713958","https://openalex.org/W2291538051","https://openalex.org/W2133034788","https://openalex.org/W1257380361","https://openalex.org/W3005559199","https://openalex.org/W2790969636"],"abstract_inverted_index":{"Deep":[0],"learning":[1,26,165],"and":[2,13,77,100],"its":[3],"applications":[4],"have":[5,88],"gained":[6],"tremendous":[7],"interest":[8],"recently":[9],"in":[10,162,167],"both":[11,57],"academia":[12],"industry.":[14],"Restricted":[15],"Boltzmann":[16],"machines":[17],"(RBMs)":[18],"offer":[19,158],"a":[20,31,159],"key":[21],"methodology":[22],"to":[23],"implement":[24],"deep":[25,38,94],"paradigms.":[27],"This":[28],"brief":[29],"presents":[30],"novel":[32],"approach":[33],"for":[34,55,98,105,154],"realizing":[35,56],"hybrid":[36],"CMOS-memristive-based":[37],"generative":[39],"models":[40],"(DGMs).":[41],"In":[42],"our":[43],"proposed":[44,85,125,142,149],"DGM":[45],"architecture,":[46],"HfO<sub>x</sub>-based":[47],"(filamentary-type":[48],"switching)":[49],"memristive":[50],"devices":[51],"are":[52,152],"extensively":[53],"used":[54,153],"computational":[58],"as":[59,61],"well":[60],"storage":[62],"functions,":[63],"such":[64],"as:":[65],"1)":[66,93],"synapses":[67],"(weights);":[68],"2)":[69,101],"internal":[70],"neuron-state":[71],"storage;":[72],"3)":[73],"stochastic":[74],"neuron":[75],"activation;":[76],"4)":[78],"programmable":[79],"signal":[80],"normalization.":[81],"To":[82],"validate":[83],"the":[84,106,112,124,130,141,148],"scheme,":[86],"we":[87],"simulated":[89],"two":[90],"different":[91],"architectures:":[92],"belief":[95],"network":[96,144],"(DBN)":[97],"classification":[99],"stacked":[102],"denoising":[103],"autoencoder":[104],"reconstruction":[107],"of":[108,123,140,164],"handwritten":[109],"digits":[110],"from":[111],"MNIST":[113],"data":[114],"set.":[115],"The":[116],"maximum":[117],"test":[118],"accuracy":[119],"achieved":[120,137],"by":[121,138],"pretraining":[122,139],"DBN":[126],"was":[127,145],"92.6%,":[128],"whereas":[129],"best":[131],"case":[132],"mean":[133],"squared":[134],"error":[135],"(mse)":[136],"SDA":[143],"0.046.":[146],"When":[147],"model-based":[150],"weights":[151],"weight":[155],"initialization,":[156],"they":[157],"significant":[160],"advantage":[161],"terms":[163],"performance":[166],"comparison":[168],"with":[169],"randomized":[170],"initialization.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
