{"id":"https://openalex.org/W4399531108","doi":"https://doi.org/10.1109/nice61972.2024.10548051","title":"A Recurrent Dynamic Model for Efficient Bayesian Optimization<sup>*</sup>","display_name":"A Recurrent Dynamic Model for Efficient Bayesian Optimization<sup>*</sup>","publication_year":2024,"publication_date":"2024-04-23","ids":{"openalex":"https://openalex.org/W4399531108","doi":"https://doi.org/10.1109/nice61972.2024.10548051"},"language":"en","primary_location":{"id":"doi:10.1109/nice61972.2024.10548051","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/nice61972.2024.10548051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 Neuro Inspired Computational Elements Conference (NICE)","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/A5060580206","display_name":"P. Michael Furlong","orcid":"https://orcid.org/0000-0001-9503-8498"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"P. Michael Furlong","raw_affiliation_strings":["University of Waterloo,Center for Theoretical Neuroscience,Waterloo,Canada","Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Center for Theoretical Neuroscience,Waterloo,Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072340944","display_name":"Nicole Sandra-Yaffa Dumont","orcid":"https://orcid.org/0000-0001-5041-1527"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nicole Sandra-Yaffa Dumont","raw_affiliation_strings":["University of Waterloo,Center for Theoretical Neuroscience,Waterloo,Canada","Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Center for Theoretical Neuroscience,Waterloo,Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079548448","display_name":"Jeff Orchard","orcid":"https://orcid.org/0000-0002-4897-8951"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jeff Orchard","raw_affiliation_strings":["University of Waterloo,Center for Theoretical Neuroscience,Waterloo,Canada","Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Center for Theoretical Neuroscience,Waterloo,Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Center for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5060580206"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06549409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9988999962806702,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9980000257492065,"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/computer-science","display_name":"Computer science","score":0.5829295516014099},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5178461670875549},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5065175294876099},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27892380952835083}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5829295516014099},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5178461670875549},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5065175294876099},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27892380952835083}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nice61972.2024.10548051","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/nice61972.2024.10548051","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 Neuro Inspired Computational Elements Conference (NICE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1552864365","https://openalex.org/W1573706465","https://openalex.org/W1597286183","https://openalex.org/W1606994719","https://openalex.org/W2070862086","https://openalex.org/W2078150668","https://openalex.org/W2102227134","https://openalex.org/W2144902422","https://openalex.org/W2146600851","https://openalex.org/W2157306293","https://openalex.org/W2490662969","https://openalex.org/W2978452042","https://openalex.org/W3139657805","https://openalex.org/W3168260338","https://openalex.org/W3173761280","https://openalex.org/W3183420805","https://openalex.org/W3196972418","https://openalex.org/W3214253320","https://openalex.org/W4225389525","https://openalex.org/W4233030948","https://openalex.org/W4287705645","https://openalex.org/W6636208863","https://openalex.org/W6674792215","https://openalex.org/W6681302627","https://openalex.org/W6764988152","https://openalex.org/W6768663915","https://openalex.org/W6781315403","https://openalex.org/W6787664966","https://openalex.org/W6792264366","https://openalex.org/W6796199476"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W3106461837","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Bayesian":[0,116],"optimization":[1,6,117],"is":[2,104],"an":[3,27,52,71],"important":[4],"black-box":[5],"method":[7],"used":[8],"in":[9,129],"active":[10,126],"learning.":[11],"An":[12],"implementation":[13,34,42,53,72,114],"of":[14,54,86,93,115,125],"the":[15,74,84,87,99,113,123],"algorithm":[16,56,88],"using":[17,89],"vector":[18],"embeddings":[19],"from":[20],"Vector":[21],"Symbolic":[22],"Architectures":[23],"was":[24],"proposed":[25],"as":[26,58],"efficient,":[28],"neuromorphic":[29,80,120],"approach":[30],"to":[31,40,66,101,112],"solving":[32],"these":[33],"problems.":[35],"However,":[36],"a":[37,109],"clear":[38],"path":[39,110],"neural":[41,67],"has":[43],"not":[44],"been":[45],"explicated.":[46],"In":[47],"this":[48,55],"paper,":[49],"we":[50],"explore":[51],"expressed":[57],"recurrent":[59],"dynamics":[60],"that":[61,98],"can":[62],"be":[63],"easily":[64],"translated":[65],"populations,":[68],"and":[69,96],"present":[70],"within":[73],"Lava":[75],"programming":[76],"framework":[77],"for":[78],"Intel\u2019s":[79],"computers.":[81],"We":[82],"compare":[83],"performance":[85],"different":[90],"resolution":[91],"representations":[92],"real-valued":[94],"data,":[95],"demonstrate":[97],"ability":[100],"find":[102],"optima":[103],"preserved.":[105],"This":[106],"work":[107],"provides":[108],"forward":[111],"on":[118],"low-power":[119],"computers,":[121],"permitting":[122],"deployment":[124],"learning":[127],"techniques":[128],"low-power,":[130],"edge":[131],"computing":[132],"applications.":[133]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}