{"id":"https://openalex.org/W53293654","doi":null,"title":"Preserving Modes and Messages via Diverse Particle Selection","display_name":"Preserving Modes and Messages via Diverse Particle Selection","publication_year":2014,"publication_date":"2014-06-21","ids":{"openalex":"https://openalex.org/W53293654","mag":"53293654"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.490.9548","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.490.9548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cs.brown.edu/~sudderth/papers/icml14dpmp.pdf","raw_type":"text"},"volume":null,"type":"article","indexed_in":[],"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/A5021193065","display_name":"Jason L. Pacheco","orcid":"https://orcid.org/0000-0003-1711-1041","observed_orcids":["https://orcid.org/0000-0003-1711-1041"]},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason Pacheco","raw_affiliation_strings":["Department of Computer Science, Brown University, Providence, RI. 02912 USA#TAB#"],"raw_orcid":"https://orcid.org/0000-0003-1711-1041","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Brown University, Providence, RI. 02912 USA#TAB#","institution_ids":["https://openalex.org/I27804330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011219092","display_name":"Silvia Zuffi","orcid":"https://orcid.org/0000-0003-1358-0828","observed_orcids":["https://orcid.org/0000-0003-1358-0828"]},"institutions":[{"id":"https://openalex.org/I149899117","display_name":"Max Planck Society","ror":"https://ror.org/01hhn8329","country_code":"DE","type":"nonprofit","lineage":["https://openalex.org/I149899117"]},{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Silvia Zuffi","raw_affiliation_strings":["Max Planck Institute for Intelligent Systems, 72076 Tubingen, Germany; ITC-CNR, 20133 Milan, Italy ?"],"raw_orcid":"https://orcid.org/0000-0003-1358-0828","affiliations":[{"raw_affiliation_string":"Max Planck Institute for Intelligent Systems, 72076 Tubingen, Germany; ITC-CNR, 20133 Milan, Italy ?","institution_ids":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065396778","display_name":"Michael J. Black","orcid":"https://orcid.org/0000-0001-6077-4540","observed_orcids":["https://orcid.org/0000-0001-6077-4540"]},"institutions":[{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael J. Black","raw_affiliation_strings":["Max-Planck-Institute for Intelligent Systems, 72076 T\u00fcbingen, Germany"],"raw_orcid":"https://orcid.org/0000-0001-6077-4540","affiliations":[{"raw_affiliation_string":"Max-Planck-Institute for Intelligent Systems, 72076 T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076761279","display_name":"Erik B. Sudderth","orcid":"https://orcid.org/0000-0002-0595-9726","observed_orcids":["https://orcid.org/0000-0002-0595-9726"]},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erik B. Sudderth","raw_affiliation_strings":["Department of Computer Science, Brown University, Providence, RI. 02912 USA#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-0595-9726","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Brown University, Providence, RI. 02912 USA#TAB#","institution_ids":["https://openalex.org/I27804330"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7778,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.90297506,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1152","last_page":"1160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9247999787330627,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9247999787330627,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.023900000378489494,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.007199999876320362,"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/max-product-algorithm","display_name":"max-product algorithm","score":0.8119999766349792},{"id":"https://openalex.org/keywords/particle-selection","display_name":"particle selection","score":0.7789999842643738},{"id":"https://openalex.org/keywords/probabilistic-graphical-models","display_name":"probabilistic graphical models","score":0.6460000276565552},{"id":"https://openalex.org/keywords/message-passing","display_name":"message passing","score":0.5979999899864197},{"id":"https://openalex.org/keywords/integer-programming","display_name":"integer programming","score":0.699999988079071},{"id":"https://openalex.org/keywords/hypothesis-selection","display_name":"hypothesis selection","score":0.5249999761581421},{"id":"https://openalex.org/keywords/particle-degeneracy","display_name":"particle degeneracy","score":0.6710000038146973},{"id":"https://openalex.org/keywords/human-pose-estimation","display_name":"human pose estimation","score":0.9380000233650208},{"id":"https://openalex.org/keywords/continuous-variables","display_name":"continuous variables","score":0.3889999985694885},{"id":"https://openalex.org/keywords/initialization-robustness","display_name":"initialization robustness","score":0.753000020980835}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.6822113990783691},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6466436982154846},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6363711953163147},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6275302171707153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.525920033454895},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4964020848274231},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.49148428440093994},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.4459022879600525},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.44375649094581604},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4280101954936981},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35597580671310425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2989071011543274},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21461540460586548},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.490.9548","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.490.9548","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cs.brown.edu/~sudderth/papers/icml14dpmp.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.650.8311","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.650.8311","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://files.is.tue.mpg.de/black/papers/ICML2014.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.672.8050","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.8050","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cs.brown.edu/%7Esudderth/papers/icml14dpmp.pdf","raw_type":"text"},{"id":"pmh:oai:pure.mpg.de:item_2086972","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11858/00-001M-0000-0024-DD2B-6","pdf_url":null,"source":{"id":"https://openalex.org/S4306400654","display_name":"MPG.PuRe (Max Planck Society)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings of the 31st International Conference on Machine Learning (ICML 2014)","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"mag:53293654","doi":null,"is_oa":false,"landing_page_url":"http://jmlr.org/proceedings/papers/v32/pacheco14.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Machine Learning","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W94449998","https://openalex.org/W1603514996","https://openalex.org/W1618905105","https://openalex.org/W1846689784","https://openalex.org/W1989191365","https://openalex.org/W2013640163","https://openalex.org/W2020999234","https://openalex.org/W2022699039","https://openalex.org/W2043217799","https://openalex.org/W2055936398","https://openalex.org/W2062122868","https://openalex.org/W2073202561","https://openalex.org/W2087112735","https://openalex.org/W2090549674","https://openalex.org/W2099305880","https://openalex.org/W2106317217","https://openalex.org/W2107884096","https://openalex.org/W2108190040","https://openalex.org/W2120340025","https://openalex.org/W2121812207","https://openalex.org/W2125712079","https://openalex.org/W2131297510","https://openalex.org/W2135194391","https://openalex.org/W2150734839","https://openalex.org/W2154515666","https://openalex.org/W2161508606","https://openalex.org/W2161969291","https://openalex.org/W2167947196"],"related_works":["https://openalex.org/W1835958559","https://openalex.org/W7100528293","https://openalex.org/W2136160685","https://openalex.org/W3177416220","https://openalex.org/W2003095743","https://openalex.org/W7100740874","https://openalex.org/W7220978653","https://openalex.org/W94449998","https://openalex.org/W7100664620","https://openalex.org/W7098605172"],"abstract_inverted_index":{"In":[0],"applications":[1],"of":[2,22,37,51,96,136],"graphical":[3],"models":[4],"arising":[5],"in":[6,79,89,130],"do-mains":[7],"such":[8],"as":[9],"computer":[10],"vision":[11],"and":[12,41,61],"signal":[13],"pro-cessing,":[14],"we":[15,111],"often":[16],"seek":[17],"the":[18,49,90,103,134],"most":[19],"likely":[20],"config-urations":[21],"high-dimensional,":[23],"continuous":[24],"vari-ables.":[25],"We":[26],"develop":[27],"a":[28,34,120],"particle-based":[29,128],"max-product":[30,81],"algorithm":[31],"which":[32],"maintains":[33],"diverse":[35],"set":[36,50],"pos-terior":[38],"mode":[39],"hypotheses,":[40,92],"is":[42,56],"robust":[43],"to":[44],"initial-ization.":[45],"At":[46],"each":[47,54],"iteration,":[48],"hypothe-ses":[52],"at":[53],"node":[55],"augmented":[57],"via":[58,64],"stochastic":[59,104],"pro-posals,":[60],"then":[62],"reduced":[63],"an":[65],"efficient":[66],"selec-tion":[67],"algorithm.":[68],"The":[69],"integer":[70],"program":[71],"underlying":[72,107],"our":[73],"optimization-based":[74],"particle":[75,108],"selection":[76],"mini-mizes":[77],"errors":[78],"subsequent":[80],"mes-sage":[82],"updates.":[83],"This":[84],"objective":[85],"automatically":[86],"en-courages":[87],"diversity":[88],"maintained":[91],"without":[93],"requiring":[94],"tuning":[95],"application-specific":[97],"distances":[98],"among":[99],"hypotheses.":[100],"By":[101],"avoiding":[102],"resampling":[105],"steps":[106],"sum-product":[109],"algorithms,":[110],"also":[112],"avoid":[113],"common":[114],"degeneracies":[115],"where":[116],"particles":[117],"collapse":[118],"onto":[119],"sin-gle":[121],"hypothesis.":[122],"Our":[123],"approach":[124],"significantly":[125],"out-performs":[126],"previous":[127],"algorithms":[129],"experiments":[131],"focusing":[132],"on":[133],"estimation":[135],"human":[137],"pose":[138],"from":[139],"single":[140],"images.":[141],"1.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":6}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
