{"id":"https://openalex.org/W7133333345","doi":"https://doi.org/10.48550/arxiv.2603.01759","title":"Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning","display_name":"Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133333345","doi":"https://doi.org/10.48550/arxiv.2603.01759"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01759","pdf_url":null,"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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01759","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127953859","display_name":"Zichen Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Zichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127947033","display_name":"Yaoyao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yaoyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128030818","display_name":"Qianru Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Qianru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.5830000042915344,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.5830000042915344,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2815000116825104,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0272000003606081,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/hyperparameter","display_name":"Hyperparameter","score":0.8528000116348267},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6227999925613403},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.538100004196167},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5131000280380249},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3869999945163727},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.3828999996185303},{"id":"https://openalex.org/keywords/scratch","display_name":"Scratch","score":0.38089999556541443},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3490000069141388}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.8528000116348267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7651000022888184},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6227999925613403},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.538100004196167},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5131000280380249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5123999714851379},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4397999942302704},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4066999852657318},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3375000059604645},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C107464732","wikidata":"https://www.wikidata.org/wiki/Q235781","display_name":"Adaptive control","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2540999948978424},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01759","pdf_url":null,"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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01759","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01759","pdf_url":null,"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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Training":[0],"large":[1],"foundation":[2],"models":[3,31],"from":[4],"scratch":[5],"for":[6],"domain-specific":[7],"applications":[8],"is":[9,35],"almost":[10],"impossible":[11],"due":[12],"to":[13,90],"data":[14],"limits":[15],"and":[16,43,59,74,126,149,155,178],"long-tailed":[17],"distributions":[18],"--":[19,67],"taking":[20],"remote":[21],"sensing":[22],"(RS)":[23],"as":[24,57,69,82],"an":[25],"example.":[26],"Fine-tuning":[27],"natural":[28,156],"image":[29,157],"pre-trained":[30],"on":[32,46,84,119,145],"RS":[33,85,120,154],"images":[34,86],"a":[36,99,172],"straightforward":[37],"solution.":[38],"To":[39,93],"reduce":[40],"computational":[41],"costs":[42],"improve":[44],"performance":[45,166],"tail":[47],"classes,":[48],"existing":[49],"methods":[50],"apply":[51],"parameter-efficient":[52],"fine-tuning":[53,83],"(PEFT)":[54],"techniques,":[55],"such":[56,68],"LoRA":[58],"AdaptFormer.":[60],"However,":[61],"we":[62,96],"observe":[63],"that":[64,104,162],"fixed":[65],"hyperparameters":[66],"intra-layer":[70],"positions,":[71],"layer":[72,124],"depth,":[73],"scaling":[75],"factors,":[76],"can":[77],"considerably":[78],"hinder":[79],"PEFT":[80,118,136],"performance,":[81],"proves":[87],"highly":[88],"sensitive":[89],"these":[91],"settings.":[92],"address":[94],"this,":[95],"propose":[97],"MetaPEFT,":[98],"method":[100],"incorporating":[101],"adaptive":[102],"scalers":[103],"dynamically":[105,112],"adjust":[106],"module":[107,122],"influence":[108,134],"during":[109],"fine-tuning.":[110],"MetaPEFT":[111,163],"adjusts":[113],"three":[114,146],"key":[115],"factors":[116],"of":[117,135,175],"images:":[121],"insertion,":[123],"selection,":[125],"module-wise":[127],"learning":[128],"rates,":[129],"which":[130],"collectively":[131],"control":[132],"the":[133,139],"modules":[137],"across":[138],"network.":[140],"We":[141],"conduct":[142],"extensive":[143],"experiments":[144],"transfer-learning":[147],"scenarios":[148],"five":[150],"datasets":[151],"in":[152,167],"both":[153],"domains.":[158],"The":[159],"results":[160],"show":[161],"achieves":[164],"state-of-the-art":[165],"cross-spectral":[168],"adaptation,":[169],"requiring":[170],"only":[171],"small":[173],"amount":[174],"trainable":[176],"parameters":[177],"improving":[179],"tail-class":[180],"accuracy":[181],"significantly.":[182]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-04T00:00:00"}
