{"id":"https://openalex.org/W7118005022","doi":"https://doi.org/10.48550/arxiv.2512.24866","title":"Characterization of Transfer Using Multi-task Learning Curves","display_name":"Characterization of Transfer Using Multi-task Learning Curves","publication_year":2025,"publication_date":"2025-12-31","ids":{"openalex":"https://openalex.org/W7118005022","doi":"https://doi.org/10.48550/arxiv.2512.24866"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.24866","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24866","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":null,"license_id":null,"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.2512.24866","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121791440","display_name":"Andr\u00e1s Millinghoffer","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Millinghoffer, Andr\u00e1s","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073373710","display_name":"Bence Bolg\u00e1r","orcid":"https://orcid.org/0000-0002-9561-1095"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bolg\u00e1r, Bence","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121756717","display_name":"P\u00e9ter Antal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antal, P\u00e9ter","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121791440"],"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5430999994277954,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5430999994277954,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0885000005364418,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.039900001138448715,"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/transfer-of-learning","display_name":"Transfer of learning","score":0.6897000074386597},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6342999935150146},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5787000060081482},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5552999973297119},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5498999953269958},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.4575999975204468},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4481000006198883},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42649999260902405},{"id":"https://openalex.org/keywords/characterization","display_name":"Characterization (materials science)","score":0.42289999127388}],"concepts":[{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6897000074386597},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6342999935150146},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5787000060081482},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5552999973297119},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5498999953269958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5257999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5076000094413757},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43529999256134033},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C2780841128","wikidata":"https://www.wikidata.org/wiki/Q5073781","display_name":"Characterization (materials science)","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40400001406669617},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.3977999985218048},{"id":"https://openalex.org/C34585555","wikidata":"https://www.wikidata.org/wiki/Q1368723","display_name":"Learning curve","level":2,"score":0.38530001044273376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37779998779296875},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.37229999899864197},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C2779066501","wikidata":"https://www.wikidata.org/wiki/Q1761267","display_name":"Transfer problem","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C21563000","wikidata":"https://www.wikidata.org/wiki/Q484511","display_name":"Inductive reasoning","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3449000120162964},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C77075516","wikidata":"https://www.wikidata.org/wiki/Q6027324","display_name":"Inductive transfer","level":5,"score":0.2913999855518341},{"id":"https://openalex.org/C184389593","wikidata":"https://www.wikidata.org/wiki/Q603159","display_name":"Curve fitting","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.2648000121116638},{"id":"https://openalex.org/C81299745","wikidata":"https://www.wikidata.org/wiki/Q334269","display_name":"Transfer function","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.24866","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24866","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.24866","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.24866","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":null,"license_id":null,"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":{"Transfer":[0],"effects":[1,56,133,147],"manifest":[2],"themselves":[3],"both":[4],"during":[5,87],"training":[6],"using":[7,16,57,116],"a":[8,39,117],"fixed":[9],"data":[10,24,121],"set":[11,25],"and":[12,41,93,110,137,144],"in":[13,148],"inductive":[14,63],"inference":[15],"accumulating":[17],"data.":[18],"We":[19,69,89],"hypothesize":[20],"that":[21,126],"perturbing":[22,32],"the":[23,33,62,81,91,105,132],"by":[26,35],"including":[27],"more":[28,42],"samples,":[29],"instead":[30],"of":[31,45,134],"model":[34,54],"gradient":[36],"updates,":[37],"provides":[38],"complementary":[40],"fundamental":[43],"characterization":[44],"transfer":[46,55,146],"effects.":[47],"To":[48],"capture":[49,131],"this":[50],"phenomenon,":[51],"we":[52],"quantitatively":[53],"multi-task":[58,76,135,139],"learning":[59,77,127,136],"curves":[60,78,128],"approximating":[61],"performance":[64],"over":[65],"varying":[66],"sample":[67],"sizes.":[68],"describe":[70],"an":[71],"efficient":[72],"method":[73,85],"to":[74,80,96],"approximate":[75],"analogous":[79],"Task":[82],"Affinity":[83],"Grouping":[84],"applied":[86],"training.":[88],"compare":[90],"statistical":[92],"computational":[94],"approaches":[95],"transfer,":[97],"which":[98],"indicates":[99],"considerably":[100],"higher":[101],"compute":[102],"costs":[103],"for":[104],"previous":[106],"but":[107],"better":[108,130],"power":[109],"broader":[111],"applicability.":[112],"Evaluations":[113],"are":[114],"performed":[115],"benchmark":[118],"drug-target":[119],"interaction":[120],"set.":[122],"Our":[123],"results":[124],"show":[125],"can":[129,141],"their":[138],"extensions":[140],"delineate":[142],"pairwise":[143],"contextual":[145],"foundation":[149],"models.":[150]},"counts_by_year":[],"updated_date":"2026-01-02T23:15:32.796280","created_date":"2026-01-02T00:00:00"}
