{"id":"https://openalex.org/W6886808143","doi":"https://doi.org/10.15167/alfano-paolo-didier_phd2023-05-30","title":"Efficient machine learning with resources constraints","display_name":"Efficient machine learning with resources constraints","publication_year":2023,"publication_date":"2023-05-30","ids":{"openalex":"https://openalex.org/W6886808143","doi":"https://doi.org/10.15167/alfano-paolo-didier_phd2023-05-30"},"language":"en","primary_location":{"id":"pmh:oai:iris.unige.it:11567/1119375","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1119375","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/11567/1119375","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"ALFANO, PAOLO DIDIER","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"ALFANO, PAOLO DIDIER","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5561000108718872},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.5324000120162964},{"id":"https://openalex.org/keywords/impossibility","display_name":"Impossibility","score":0.5306000113487244},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5169000029563904},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47519999742507935},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.430400013923645},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.41110000014305115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7512999773025513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.645799994468689},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.616100013256073},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5561000108718872},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.5324000120162964},{"id":"https://openalex.org/C2776261394","wikidata":"https://www.wikidata.org/wiki/Q315562","display_name":"Impossibility","level":2,"score":0.5306000113487244},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5169000029563904},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47519999742507935},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.430400013923645},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3334999978542328},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33219999074935913},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.26260000467300415}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:iris.unige.it:11567/1119375","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1119375","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},{"id":"doi:10.15167/alfano-paolo-didier_phd2023-05-30","is_oa":true,"landing_page_url":"https://doi.org/10.15167/alfano-paolo-didier_phd2023-05-30","pdf_url":null,"source":{"id":"https://openalex.org/S7407050993","display_name":"Universit\u00e0 degli Studi di Genova","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:iris.unige.it:11567/1119375","is_oa":true,"landing_page_url":"https://hdl.handle.net/11567/1119375","pdf_url":null,"source":{"id":"https://openalex.org/S4377196291","display_name":"CINECA IRIS Institutial Research Information System (University of Genoa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83816512","host_organization_name":"University of Genoa","host_organization_lineage":["https://openalex.org/I83816512"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Since":[0],"machine":[1,257,484],"learning":[2,258,271,485],"techniques":[3,23,480],"spread":[4],"in":[5,10,60,79,137,151,181,203,221,230,350,357,367,471,488],"the":[6,19,31,38,61,64,76,120,141,208,215,222,237,251,340,347,436,496],"scientific":[7,497],"community":[8],"and":[9,100,115,313,323,415,443],"real-world":[11],"scenarios,":[12],"their":[13],"usage":[14],"has":[15,94,131],"been":[16,95,132],"justified":[17],"by":[18,144,154,407],"impossibility":[20],"of":[21,33,44,49,66,83,92,164,168,175,179,196,205,210,232,254,316,404,438,479],"traditional":[22],"to":[24,55,71,88,103,128,140,177,194,199,213,235,249,332,371,388,422,446,451,490,495],"deal":[25],"with":[26,51,187,305,336],"simple":[27,99,269,432],"problems":[28,93],"that":[29,273,296,309,328,411,481],"require":[30],"retrieval":[32],"specific":[34,101],"task-related":[35],"information.":[36],"In":[37,119,241],"beginning,":[39],"neural":[40,146],"networks":[41],"were":[42],"made":[43],"a":[45,52,80,124,159,191,227,268,281,306,333,351,402,466,477],"very":[46,98,444],"reduced":[47,337],"amount":[48,209],"layers,":[50],"limited":[53],"capacity":[54],"solve":[56,89],"complicated":[57,445],"problems.":[58,259],"However,":[59],"last":[62,121,223],"years,":[63],"set":[65,403,478],"methodologies":[67,248,387,421],"we":[68,244,261,293,320,383,412,464,474],"usually":[69],"refer":[70],"as":[72,108,129,278,375,380,401,461,463],"\\\\textit{deep":[73],"learning}":[74],"became":[75],"de-facto":[77],"standard":[78],"large":[81],"variety":[82],"fields.":[84],"Their":[85],"astonishing":[86],"ability":[87],"different":[90,162,247,386,420,455],"kinds":[91],"proven,":[96],"from":[97,161,173,190],"tasks":[102,433],"more":[104,216,287,392],"general":[105],"problems,":[106],"such":[107,424],"image":[109,265,393,456],"recognition,":[110,114],"object":[111],"detection,":[112],"video":[113],"natural":[116],"language":[117],"processing.":[118],"two":[122,314,390],"years":[123,185,225],"new":[125],"approach,":[126],"referred":[127],"transformers,":[130],"proposed":[133],"showing":[134,226],"state-of-the-art":[135,239],"performances":[136,152],"similar":[138],"contexts":[139],"ones":[142],"covered":[143],"convolutional":[145,276],"networks.":[147],"The":[148,166],"huge":[149],"improvement":[150],"obtained":[153],"recent":[155,217],"models":[156,202],"came":[157],"at":[158],"cost":[160],"points":[163,405],"view.":[165],"number":[167],"learnable":[169],"parameters":[170],"involved":[171],"moved":[172],"tens":[174],"millions":[176,195],"hundreds":[178],"billions":[180],"less":[182],"than":[183,288],"ten":[184],"coupled":[186],"an":[188,325,358,408],"increase":[189],"few":[192,224],"hundred":[193],"PFLOPS":[197],"needed":[198,212,234],"train":[200,214],"better":[201],"terms":[204,231],"performance.":[206,240],"Overall,":[207,470],"energy":[211],"architectures":[218],"increased":[219],"drastically":[220],"problematic":[228],"situation":[229],"resources":[233],"obtain":[236,452],"next":[238],"this":[242,297,472],"thesis,":[243,473],"will":[245,262,294,321,345,384,418,427,475],"see":[246],"alleviate":[250,483],"computational":[252,486],"costs":[253],"some":[255],"typical":[256],"First,":[260],"focus":[263],"on":[264,431,454],"classification,":[266],"considering":[267],"transfer":[270],"approach":[272,299],"exploits":[274],"pre-trained":[275],"features":[277],"input":[279,330],"for":[280],"fast":[282],"kernel":[283],"method.":[284],"By":[285],"performing":[286],"three":[289],"thousand":[290],"training":[291,307],"processes,":[292],"show":[295,346,428],"fast-kernel":[298],"provides":[300],"comparable":[301],"accuracy":[302],"w.r.t.":[303],"fine-tuning,":[304],"time":[308],"is":[310,361,369,441,449],"between":[311],"one":[312],"orders":[315],"magnitude":[317],"smaller.":[318],"Then":[319],"introduce":[322,419],"discuss":[324,385],"unsupervised":[326,359],"pipeline":[327,348],"projects":[329],"images":[331],"latent":[334],"space":[335],"dimension,":[338],"making":[339],"clustering":[341],"operation":[342],"doable.":[343],"We":[344,417,426],"effectiveness":[349],"plankton":[352,365],"monitoring":[353],"context":[354],"where":[355],"operating":[356],"manner":[360],"crucial.":[362],"Indeed,":[363,395],"studying":[364],"population":[366],"situ":[368],"paramount":[370],"protect":[372],"marine":[373],"ecosystems":[374],"they":[376],"can":[377,398,413,482],"be":[378,399],"regarded":[379],"biosensors.":[381],"Lastly,":[382],"compare":[389],"or":[391],"datasets.":[394],"each":[396],"dataset":[397,439],"seen":[400],"sampled":[406],"unknown":[409],"distribution":[410],"estimate":[414],"analyze.":[416],"study":[423],"distributions.":[425],"that,":[429],"even":[430],"involving":[434],"images,":[435],"concept":[437],"distance":[440],"elusive":[442],"quantify.":[447],"It":[448],"possible":[450],"information":[453],"datasets,":[457],"via":[458],"good":[459],"partitioning,":[460],"long":[462],"analyze":[465],"small":[467],"datasets":[468],"subset.":[469],"consider":[476],"costs,":[487],"order":[489],"keep":[491],"them":[492],"computationally":[493],"accessible":[494],"community.":[498]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
