{"id":"https://openalex.org/W4387870774","doi":"https://doi.org/10.1109/mlsp55844.2023.10285914","title":"Gflownets for Sensor Selection","display_name":"Gflownets for Sensor Selection","publication_year":2023,"publication_date":"2023-09-17","ids":{"openalex":"https://openalex.org/W4387870774","doi":"https://doi.org/10.1109/mlsp55844.2023.10285914"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp55844.2023.10285914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55844.2023.10285914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5085250431","display_name":"Spilios Evmorfos","orcid":"https://orcid.org/0000-0002-9899-4562"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Spilios Evmorfos","raw_affiliation_strings":["Rutgers, The State University of New Jersey"],"affiliations":[{"raw_affiliation_string":"Rutgers, The State University of New Jersey","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067199896","display_name":"Zhaoyi Xu","orcid":"https://orcid.org/0000-0001-6327-7670"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhaoyi Xu","raw_affiliation_strings":["Rutgers, The State University of New Jersey"],"affiliations":[{"raw_affiliation_string":"Rutgers, The State University of New Jersey","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004201783","display_name":"Athina P. Petropulu","orcid":"https://orcid.org/0000-0001-7380-7815"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Athina Petropulu","raw_affiliation_strings":["Rutgers, The State University of New Jersey"],"affiliations":[{"raw_affiliation_string":"Rutgers, The State University of New Jersey","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5085250431"],"corresponding_institution_ids":["https://openalex.org/I102322142"],"apc_list":null,"apc_paid":null,"fwci":1.2037,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.80487936,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9987000226974487,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9987000226974487,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9968000054359436,"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/T11946","display_name":"Antenna Design and Optimization","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/beamforming","display_name":"Beamforming","score":0.6579364538192749},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6074082851409912},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5865825414657593},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5225867033004761},{"id":"https://openalex.org/keywords/terminal","display_name":"Terminal (telecommunication)","score":0.5020546913146973},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49224653840065},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4488877058029175},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4325874447822571},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.43199622631073},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.4118942618370056},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4037725627422333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37513467669487},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3156197667121887},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3114098906517029},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2287571132183075},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09297814965248108}],"concepts":[{"id":"https://openalex.org/C54197355","wikidata":"https://www.wikidata.org/wiki/Q5782992","display_name":"Beamforming","level":2,"score":0.6579364538192749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6074082851409912},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5865825414657593},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5225867033004761},{"id":"https://openalex.org/C2779664074","wikidata":"https://www.wikidata.org/wiki/Q3518405","display_name":"Terminal (telecommunication)","level":2,"score":0.5020546913146973},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49224653840065},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4488877058029175},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4325874447822571},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.43199622631073},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.4118942618370056},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4037725627422333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37513467669487},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3156197667121887},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3114098906517029},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2287571132183075},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09297814965248108},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp55844.2023.10285914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55844.2023.10285914","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8799999952316284,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W43927996","https://openalex.org/W1522301498","https://openalex.org/W2009480643","https://openalex.org/W2013943064","https://openalex.org/W2040375412","https://openalex.org/W2169207653","https://openalex.org/W2477563955","https://openalex.org/W2605375285","https://openalex.org/W2657877489","https://openalex.org/W2673029499","https://openalex.org/W2949582403","https://openalex.org/W2963689780","https://openalex.org/W3036843665","https://openalex.org/W3122971299","https://openalex.org/W3128096176","https://openalex.org/W3166129236","https://openalex.org/W4205234875","https://openalex.org/W4224925138","https://openalex.org/W4287123203","https://openalex.org/W6631190155","https://openalex.org/W6755308174","https://openalex.org/W6780179280","https://openalex.org/W6789897983","https://openalex.org/W6796203516"],"related_works":["https://openalex.org/W3211557223","https://openalex.org/W2007036916","https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2413828414","https://openalex.org/W2367222340","https://openalex.org/W187740018","https://openalex.org/W2162286586","https://openalex.org/W4255368532"],"abstract_inverted_index":{"The":[0,66,84,128,155],"efficacy":[1],"of":[2,12,34,47,113,124,138,169,176],"sensor":[3,58,78],"arrays":[4,24],"improves":[5],"with":[6],"more":[7],"elements,":[8],"yet":[9],"increased":[10],"number":[11],"elements":[13],"leads":[14],"to":[15,92,120,144],"higher":[16],"computational":[17],"demands,":[18],"cost":[19],"and":[20,151],"power":[21],"consumption.":[22],"Sparse":[23],"offer":[25],"a":[26,32,40,62,72,94,115,139,152,166],"cost-effective":[27],"solution":[28],"by":[29,162],"utilizing":[30],"only":[31],"subset":[33,38,140],"available":[35],"elements.":[36],"Each":[37],"has":[39],"different":[41],"effect":[42],"on":[43,61,99,165],"the":[44,48,100,105,110,121,125,136,145,170,177,181],"performance":[45,123,137],"properties":[46],"array.":[49],"This":[50],"paper":[51],"presents":[52],"an":[53],"unsupervised":[54],"learning":[55],"approach":[56,129],"for":[57,132,180],"selection":[59,67],"based":[60,98],"deep":[63],"generative":[64],"modeling.":[65],"process":[68],"is":[69,90,118,130,141],"treated":[70],"as":[71,81],"deterministic":[73],"Markov":[74],"Decision":[75],"Process,":[76],"where":[77,135],"subarrays":[79],"arise":[80],"terminal":[82,116],"states.":[83],"Generative":[85],"Flow":[86],"Network":[87],"(GFlowNet)":[88],"paradigm":[89],"employed":[91],"learn":[93],"distribution":[95,107],"over":[96],"actions":[97],"current":[101],"state.":[102],"Sampling":[103],"from":[104],"aforementioned":[106],"ensures":[108],"that":[109],"cumulative":[111],"probability":[112],"reaching":[114],"state":[117],"proportional":[119,143],"sensing":[122],"corresponding":[126,149],"subset.":[127],"applied":[131],"transmit":[133],"beamforming":[134],"inversely":[142],"error":[146],"between":[147],"its":[148],"beampattern":[150],"desired":[153],"beampattern.":[154],"method":[156],"can":[157],"generate":[158],"multiple":[159],"high-performing":[160],"subsets":[161,172,179],"being":[163],"trained":[164],"small":[167],"percentage":[168],"possible":[171,178],"(less":[173],"than":[174],"0.0001%":[175],"conducted":[182],"experiments).":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}