iBet uBet
web content aggregator. Adding the entire web to your favor.
Link to original content:
https://api.openalex.org/works/doi:10.48550/ARXIV.2406.12008
{"id":"https://openalex.org/W4399836750","doi":"https://doi.org/10.48550/arxiv.2406.12008","title":"QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining\n of Random Forest","display_name":"QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining\n of Random Forest","publication_year":2024,"publication_date":"2024-06-17","ids":{"openalex":"https://openalex.org/W4399836750","doi":"https://doi.org/10.48550/arxiv.2406.12008"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2406.12008","pdf_url":"https://arxiv.org/pdf/2406.12008","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2406.12008","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039033610","display_name":"Romina Yalovetzky","orcid":"https://orcid.org/0000-0001-8397-2072"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yalovetzky, Romina","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100596875","display_name":"Niran Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Niran","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101541320","display_name":"Changhao Li","orcid":"https://orcid.org/0000-0002-3019-5887"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Changhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066501364","display_name":"Marco Pistoia","orcid":"https://orcid.org/0000-0001-9002-1128"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pistoia, Marco","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":86},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Network Fundamentals and Applications","score":0.974,"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/T10320","display_name":"Neural Network Fundamentals and Applications","score":0.974,"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/T10876","display_name":"Process Fault Detection and Diagnosis in Industries","score":0.9228,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T12676","display_name":"Theory and Applications of Extreme Learning Machines","score":0.9128,"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/speedup","display_name":"Speedup","score":0.89987236},{"id":"https://openalex.org/keywords/backpropagation-learning","display_name":"Backpropagation Learning","score":0.571833},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.562949},{"id":"https://openalex.org/keywords/online-sequential-learning","display_name":"Online Sequential Learning","score":0.562124},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"Recurrent Neural Networks","score":0.555414},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental Learning","score":0.550325},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.42621595}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.89987236},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.56657624},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.5058465},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46648386},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.42621595},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36947304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26224583},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.15949541},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.11081073},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.06597158},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.05712256},{"id":"https://openalex.org/C155202549","wikidata":"https://www.wikidata.org/wiki/Q178803","display_name":"International trade","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2406.12008","pdf_url":"https://arxiv.org/pdf/2406.12008","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2406.12008","pdf_url":"https://arxiv.org/pdf/2406.12008","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W98480971","https://openalex.org/W3046370962","https://openalex.org/W2597809628","https://openalex.org/W2164382479","https://openalex.org/W2157978810","https://openalex.org/W2150291671","https://openalex.org/W2146343568","https://openalex.org/W2058965144","https://openalex.org/W2027972911","https://openalex.org/W2013643406"],"abstract_inverted_index":{"Random":[0],"Forest":[1],"(RF)":[2],"is":[3,34,41,73,89],"a":[4,110,129,142,183],"popular":[5],"tree-ensemble":[6],"method":[7,175],"for":[8,12,25,95,123,145],"supervised":[9],"learning,":[10],"prized":[11],"its":[13],"ease":[14],"of":[15,105,136],"use":[16],"and":[17,43,55,69,126,149,170],"flexibility.":[18],"Online":[19],"RF":[20,96,117,204],"models":[21,118],"require":[22],"to":[23,29,114,158,167,176,202,213],"account":[24],"new":[26,70],"training":[27],"data":[28,40,49,71,83],"maintain":[30],"model":[31,64,221],"accuracy.":[32],"This":[33],"particularly":[35],"important":[36],"in":[37,48,81,119,132,200],"applications":[38],"were":[39],"periodically":[42],"sequentially":[44],"generated":[45],"over":[46,85],"time":[47],"streams,":[50],"such":[51],"as":[52,75,97,161],"auto-driving":[53],"systems,":[54],"credit":[56],"card":[57],"payments.":[58],"In":[59],"this":[60,88],"setting,":[61],"performing":[62],"periodic":[63],"retraining":[65,150],"with":[66,91,101,211],"the":[67,82,102,120,133,162,188,220],"old":[68],"accumulated":[72,103,137],"beneficial":[74],"it":[76],"fully":[77],"captures":[78],"possible":[79],"drifts":[80],"distribution":[84],"time.":[86],"However,":[87],"unpractical":[90],"state-of-the-art":[92,203],"classical":[93,174],"algorithms":[94],"they":[98],"scale":[99],"linearly":[100],"number":[104,135],"samples.":[106,138],"We":[107],"propose":[108],"QC-Forest,":[109],"classical-quantum":[111],"algorithm":[112,144],"designed":[113],"time-efficiently":[115],"retrain":[116],"streaming":[121],"setting":[122],"multi-class":[124,159],"classification":[125],"regression,":[127],"achieving":[128],"runtime":[130],"poly-logarithmic":[131,190],"total":[134],"QC-Forest":[139,196],"leverages":[140],"Des-q,":[141],"quantum":[143,180],"single":[146],"tree":[147],"construction":[148],"proposed":[151],"by":[152,156],"Kumar":[153],"et":[154],"al.":[155],"expanding":[157],"classification,":[160],"original":[163],"proposal":[164],"was":[165],"limited":[166],"binary":[168],"classes,":[169],"introducing":[171],"an":[172,178],"exact":[173],"replace":[177],"underlying":[179],"subroutine":[181],"incurring":[182],"finite":[184],"error,":[185],"while":[186,216],"maintaining":[187],"same":[189],"dependence.":[191],"Finally,":[192],"we":[193],"showcase":[194],"that":[195],"achieves":[197],"competitive":[198],"accuracy":[199],"comparison":[201],"methods":[205],"on":[206],"widely":[207],"used":[208],"benchmark":[209],"datasets":[210],"up":[212,219],"80,000":[214],"samples,":[215],"significantly":[217],"speeding":[218],"retrain.":[222]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4399836750","counts_by_year":[],"updated_date":"2024-10-20T21:25:33.273980","created_date":"2024-06-20"}
