{"id":"https://openalex.org/W7151264117","doi":"https://doi.org/10.1109/icmla66185.2025.00146","title":"Information Theoretic Threshold Tuning in Parallel Stochastic Quantizer Architectures \u2217","display_name":"Information Theoretic Threshold Tuning in Parallel Stochastic Quantizer Architectures \u2217","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151264117","doi":"https://doi.org/10.1109/icmla66185.2025.00146"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-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/A5133122601","display_name":"Hassan Tavakoli","orcid":null},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]},{"id":"https://openalex.org/I181233156","display_name":"University of Oregon","ror":"https://ror.org/0293rh119","country_code":"US","type":"education","lineage":["https://openalex.org/I181233156"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hassan Tavakoli","raw_affiliation_strings":["Oregon State University,School of EECS,Oregon, OR,USA,97331"],"affiliations":[{"raw_affiliation_string":"Oregon State University,School of EECS,Oregon, OR,USA,97331","institution_ids":["https://openalex.org/I131249849","https://openalex.org/I181233156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133120627","display_name":"Thinh Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]},{"id":"https://openalex.org/I181233156","display_name":"University of Oregon","ror":"https://ror.org/0293rh119","country_code":"US","type":"education","lineage":["https://openalex.org/I181233156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thinh Nguyen","raw_affiliation_strings":["Oregon State University,School of EECS,Oregon, OR,USA,97331"],"affiliations":[{"raw_affiliation_string":"Oregon State University,School of EECS,Oregon, OR,USA,97331","institution_ids":["https://openalex.org/I131249849","https://openalex.org/I181233156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108277072","display_name":"Bella Bose","orcid":null},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]},{"id":"https://openalex.org/I181233156","display_name":"University of Oregon","ror":"https://ror.org/0293rh119","country_code":"US","type":"education","lineage":["https://openalex.org/I181233156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bella Bose","raw_affiliation_strings":["Oregon State University,School of EECS,Oregon, OR,USA,97331"],"affiliations":[{"raw_affiliation_string":"Oregon State University,School of EECS,Oregon, OR,USA,97331","institution_ids":["https://openalex.org/I131249849","https://openalex.org/I181233156"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5133122601"],"corresponding_institution_ids":["https://openalex.org/I131249849","https://openalex.org/I181233156"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.78192083,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"972","last_page":"977"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11321","display_name":"Error Correcting Code Techniques","score":0.807200014591217,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11321","display_name":"Error Correcting Code Techniques","score":0.807200014591217,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.06629999727010727,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.018799999728798866,"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/noise","display_name":"Noise (video)","score":0.35109999775886536},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.3009999990463257},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.2906000018119812},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.28220000863075256},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.27239999175071716},{"id":"https://openalex.org/keywords/stochastic-process","display_name":"Stochastic process","score":0.2581000030040741}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5799999833106995},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42179998755455017},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35109999775886536},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2921999990940094},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.2906000018119812},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.28220000863075256},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2572000026702881},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.4630100131034851,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1982392236","https://openalex.org/W2005293878","https://openalex.org/W2057240004","https://openalex.org/W2134179788","https://openalex.org/W2211856343","https://openalex.org/W2749342543","https://openalex.org/W2962702487","https://openalex.org/W3036930198","https://openalex.org/W3096903942","https://openalex.org/W3130985436","https://openalex.org/W3135710623","https://openalex.org/W3198898197","https://openalex.org/W4214522710","https://openalex.org/W4254197176","https://openalex.org/W4390948835"],"related_works":[],"abstract_inverted_index":{"Quantization":[0],"plays":[1],"a":[2,13,35,87,95],"central":[3],"role":[4],"in":[5],"digital":[6],"communication":[7],"by":[8],"mapping":[9],"continuous-valued":[10],"signals":[11],"to":[12,99],"finite":[14],"set":[15],"of":[16,139],"levels":[17],"with":[18,128],"minimal":[19],"distortion.":[20],"Beyond":[21],"mean-square":[22],"error,":[23],"mutual":[24,48,102,113,144],"information":[25,49,114],"between":[26],"the":[27,31,43,47,75,101,131,137],"channel":[28],"input":[29,120],"and":[30,85,111,118,156],"quantizer":[32,44,79],"output":[33],"provides":[34],"powerful":[36],"metric":[37],"for":[38,52],"signal":[39],"recovery.":[40],"However,":[41],"finding":[42],"that":[45,56,92,135],"maximizes":[46],"is":[50,159],"NP-complete":[51],"non-binary":[53,119],"inputs.":[54],"To":[55],"end,":[57],"while":[58],"not":[59],"optimal,":[60],"thresholding":[61],"schemes,":[62],"whether":[63],"single-threshold":[64,78],"or":[65],"multi-threshold,":[66],"are":[67,154],"widely":[68],"adopted.":[69],"In":[70],"this":[71],"work,":[72],"we":[73],"study":[74],"parallel":[76,140],"stochastic":[77],"architecture,":[80],"provide":[81],"some":[82],"information-theoretic":[83],"insights,":[84],"introduce":[86],"momentum-accelerated":[88],"gradient":[89],"ascent":[90],"algorithm":[91],"efficiently":[93],"tunes":[94],"single":[96],"decision":[97],"threshold":[98],"maximize":[100],"information.":[103],"We":[104,122],"demonstrate":[105],"convergence":[106],"improvements":[107],"over":[108],"exhaustive":[109],"search":[110],"quantify":[112],"gains":[115],"across":[116],"binary":[117],"distributions.":[121],"also":[123],"validate":[124],"our":[125],"theoretical":[126],"framework":[127],"simulations":[129],"on":[130],"MNIST":[132],"dataset,":[133],"demonstrating":[134],"increasing":[136],"number":[138],"quantization":[141,152],"branches,":[142],"i.e.,":[143],"information,":[145],"significantly":[146],"improves":[147],"classification":[148],"accuracy,":[149],"especially":[150],"when":[151],"thresholds":[153],"learned":[155],"training":[157],"data":[158],"limited.":[160]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}