{"id":"https://openalex.org/W4363648988","doi":"https://doi.org/10.1109/ciss56502.2023.10089705","title":"Towards an Improved Hyperdimensional Classifier for Event-Based Data","display_name":"Towards an Improved Hyperdimensional Classifier for Event-Based Data","publication_year":2023,"publication_date":"2023-03-22","ids":{"openalex":"https://openalex.org/W4363648988","doi":"https://doi.org/10.1109/ciss56502.2023.10089705"},"language":"en","primary_location":{"id":"doi:10.1109/ciss56502.2023.10089705","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ciss56502.2023.10089705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","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/A5010394321","display_name":"Neal Anwar","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Neal Anwar","raw_affiliation_strings":["University of Maryland,Perception and Robotics Group,College Park","Perception and Robotics Group, University of Maryland, College Park"],"affiliations":[{"raw_affiliation_string":"University of Maryland,Perception and Robotics Group,College Park","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Perception and Robotics Group, University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075055783","display_name":"Chethan M. Parameshwara","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chethan Parameshwara","raw_affiliation_strings":["University of Maryland,Perception and Robotics Group,College Park","Perception and Robotics Group, University of Maryland, College Park"],"affiliations":[{"raw_affiliation_string":"University of Maryland,Perception and Robotics Group,College Park","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Perception and Robotics Group, University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083553427","display_name":"Cornelia Ferm\u00fcller","orcid":"https://orcid.org/0000-0003-2044-2386"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cornelia Ferm\u00fcller","raw_affiliation_strings":["University of Maryland,Perception and Robotics Group,College Park","Perception and Robotics Group, University of Maryland, College Park"],"affiliations":[{"raw_affiliation_string":"University of Maryland,Perception and Robotics Group,College Park","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Perception and Robotics Group, University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036912867","display_name":"Yiannis Aloimonos","orcid":"https://orcid.org/0000-0002-8152-4281"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiannis Aloimonos","raw_affiliation_strings":["University of Maryland,Perception and Robotics Group,College Park","Perception and Robotics Group, University of Maryland, College Park"],"affiliations":[{"raw_affiliation_string":"University of Maryland,Perception and Robotics Group,College Park","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Perception and Robotics Group, University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010394321"],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02556355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9990000128746033,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.991599977016449,"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":0.758743405342102},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.6774448156356812},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5890915989875793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5794603824615479},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.563327968120575},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.5227615833282471},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46975424885749817},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4608016312122345},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45211493968963623},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4014856815338135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37558412551879883},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33191314339637756}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.758743405342102},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.6774448156356812},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5890915989875793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5794603824615479},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.563327968120575},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.5227615833282471},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46975424885749817},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4608016312122345},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45211493968963623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4014856815338135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37558412551879883},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33191314339637756},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ciss56502.2023.10089705","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ciss56502.2023.10089705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8999999761581421,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2013702864","https://openalex.org/W2070862086","https://openalex.org/W2154422144","https://openalex.org/W2522828268","https://openalex.org/W2973650171","https://openalex.org/W3004212813","https://openalex.org/W3035234631","https://openalex.org/W3161328488","https://openalex.org/W3204836041","https://openalex.org/W4225389525","https://openalex.org/W4293216350"],"related_works":["https://openalex.org/W4312445171","https://openalex.org/W4306295112","https://openalex.org/W2036807459","https://openalex.org/W2541791370","https://openalex.org/W2035976912","https://openalex.org/W2109974539","https://openalex.org/W2738084969","https://openalex.org/W3091691040","https://openalex.org/W2125927971","https://openalex.org/W2954664659"],"abstract_inverted_index":{"Hyperdimensional":[0],"Computing":[1],"(HDC)":[2],"is":[3],"an":[4,185],"emerging":[5],"neuroscience-inspired":[6],"framework":[7],"wherein":[8],"data":[9,85,116],"of":[10,47,112,115,145,177,201],"various":[11],"modalities":[12],"can":[13],"be":[14,45,75],"represented":[15],"uniformly":[16],"<tex>$\\text{in}$</tex>":[17],"high-dimensional":[18,170],"space":[19],"as":[20,90,117,119],"long,":[21],"redundant":[22],"holographic":[23],"vectors.":[24],"When":[25],"equipped":[26],"with":[27,52,77,174,184],"the":[28,91,107,122,143,178,197],"proper":[29],"Vector":[30],"Symbolic":[31],"Architecture":[32],"(VSA)":[33],"and":[34,57,109,166,180],"applied":[35],"to":[36,44,60,64,74,106,125,155],"neuromorphic":[37,87],"hardware,":[38],"HDC-based":[39],"networks":[40],"have":[41],"been":[42,100],"demonstrated":[43],"capable":[46],"solving":[48],"complex":[49,198],"visual":[50],"tasks":[51],"substantial":[53],"energy":[54],"efficiency":[55],"gains":[56],"increased":[58],"robustness":[59],"noise":[61],"when":[62],"compared":[63],"standard":[65],"Artificial":[66],"Neural":[67],"Networks":[68],"(ANNs).":[69],"HDC":[70],"has":[71,99],"shown":[72],"potential":[73],"used":[76],"great":[78],"efficacy":[79],"for":[80,137,196],"learning":[81],"based":[82],"on":[83,189],"spatiotemporal":[84,127,139],"from":[86,204],"sensors":[88],"such":[89],"Dynamic":[92],"Vision":[93],"Sensor":[94],"(DVS),":[95],"but":[96],"prior":[97],"work":[98],"limited":[101],"in":[102],"this":[103,113],"arena":[104],"due":[105],"complexity":[108],"unconventional":[110],"nature":[111],"type":[114],"well":[118],"difficulty":[120],"choosing":[121],"appropriate":[123],"VSA":[124],"hypervectorize":[126],"information.":[128],"We":[129,172],"present":[130],"a":[131,175],"bipolar":[132],"HD":[133,164,186,192],"encoding":[134,138,193],"mechanism":[135],"designed":[136],"data,":[140],"which":[141,159],"captures":[142],"contours":[144],"DVS-generated":[146],"time":[147],"surfaces":[148,158],"created":[149],"by":[150,153],"moving":[151],"objects":[152],"fitting":[154],"them":[156],"local":[157],"are":[160],"individually":[161],"encoded":[162],"into":[163,168],"vectors":[165],"bundled":[167],"descriptive":[169],"representations.":[171],"conclude":[173],"sketch":[176],"structure":[179],"training/inference":[181],"pipelines":[182],"associated":[183],"classifier,":[187],"predicated":[188],"our":[190],"proposed":[191],"scheme,":[194],"trained":[195],"real-world":[199],"task":[200],"pose":[202],"estimation":[203],"event":[205],"camera":[206],"data.":[207]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
