{"id":"https://openalex.org/W7119100429","doi":"https://doi.org/10.48550/arxiv.2601.03244","title":"Self-Supervised Learning from Noisy and Incomplete Data","display_name":"Self-Supervised Learning from Noisy and Incomplete Data","publication_year":2026,"publication_date":"2026-01-06","ids":{"openalex":"https://openalex.org/W7119100429","doi":"https://doi.org/10.48550/arxiv.2601.03244"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.03244","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.03244","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.2601.03244","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083772205","display_name":"Juli\u00e1n Tachella","orcid":"https://orcid.org/0000-0003-3878-9142"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tachella, Juli\u00e1n","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122085661","display_name":"Mike Davies","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Davies, Mike","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083772205"],"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/T11205","display_name":"Numerical methods in inverse problems","score":0.4237000048160553,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11205","display_name":"Numerical methods in inverse problems","score":0.4237000048160553,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.17159999907016754,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.13570000231266022,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/solver","display_name":"Solver","score":0.589900016784668},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.5845000147819519},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.5748000144958496},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5695000290870667},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49079999327659607},{"id":"https://openalex.org/keywords/problem-solver","display_name":"Problem solver","score":0.43560001254081726},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3774000108242035}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6990000009536743},{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.589900016784668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.588100016117096},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.5845000147819519},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.5748000144958496},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5695000290870667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5561000108718872},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49079999327659607},{"id":"https://openalex.org/C3019612716","wikidata":"https://www.wikidata.org/wiki/Q730920","display_name":"Problem solver","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3521000146865845},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2630999982357025},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.03244","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.03244","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.2601.03244","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.03244","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":[{"score":0.4553053081035614,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Many":[0],"important":[1],"problems":[2],"in":[3,59,118],"science":[4],"and":[5,55,114],"engineering":[6],"involve":[7],"inferring":[8],"a":[9,48,76,81,96,107],"signal":[10],"from":[11,50,83],"noisy":[12],"and/or":[13],"incomplete":[14],"observations,":[15],"where":[16],"the":[17,88],"observation":[18],"process":[19],"is":[20,68],"known.":[21],"Historically,":[22],"this":[23],"problem":[24],"has":[25],"been":[26],"tackled":[27],"using":[28],"hand-crafted":[29],"regularization":[30],"(e.g.,":[31],"sparsity,":[32],"total-variation)":[33],"to":[34],"obtain":[35],"meaningful":[36],"estimates.":[37],"Recent":[38],"data-driven":[39],"methods":[40,74,102],"often":[41],"offer":[42,75],"better":[43],"solutions":[44],"by":[45,79],"directly":[46],"learning":[47,73,80],"solver":[49,82],"examples":[51],"of":[52,99],"ground-truth":[53,64,91],"signals":[54],"associated":[56],"observations.":[57],"However,":[58],"many":[60],"real-world":[61],"applications,":[62],"obtaining":[63],"references":[65],"for":[66,90,103],"training":[67],"expensive":[69],"or":[70],"impossible.":[71],"Self-supervised":[72],"promising":[77],"alternative":[78],"measurement":[84],"data":[85],"alone,":[86],"bypassing":[87],"need":[89],"references.":[92],"This":[93],"manuscript":[94],"provides":[95],"comprehensive":[97],"summary":[98],"different":[100],"self-supervised":[101],"inverse":[104,120],"problems,":[105],"with":[106],"special":[108],"emphasis":[109],"on":[110],"their":[111],"theoretical":[112],"underpinnings,":[113],"presents":[115],"practical":[116],"applications":[117],"imaging":[119],"problems.":[121]},"counts_by_year":[],"updated_date":"2026-01-08T20:10:11.968330","created_date":"2026-01-08T00:00:00"}
