{"id":"https://openalex.org/W4386721612","doi":"https://doi.org/10.48550/arxiv.2309.05927","title":"Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals","display_name":"Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals","publication_year":2023,"publication_date":"2023-09-12","ids":{"openalex":"https://openalex.org/W4386721612","doi":"https://doi.org/10.48550/arxiv.2309.05927"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2309.05927","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.05927","pdf_url":"https://arxiv.org/pdf/2309.05927","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2309.05927","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020378699","display_name":"Ran Liu","orcid":"https://orcid.org/0000-0003-0007-3691"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023666998","display_name":"Ellen L. Zippi","orcid":"https://orcid.org/0000-0001-8246-5781"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zippi, Ellen L.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059295598","display_name":"Hadi Pouransari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pouransari, Hadi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092591127","display_name":"Chris Sandino","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sandino, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082699365","display_name":"Jingping Nie","orcid":"https://orcid.org/0000-0002-9181-8398"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nie, Jingping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110219608","display_name":"Hanlin Goh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goh, Hanlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090375977","display_name":"Erdrin Azemi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azemi, Erdrin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086952383","display_name":"Ali Moin","orcid":"https://orcid.org/0000-0002-2672-9030"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moin, Ali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9679999947547913,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9679999947547913,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11309","display_name":"Music and Audio Processing","score":0.919700026512146,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7752188444137573},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.6492279171943665},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5199043154716492},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.5120354294776917},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.50905841588974},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5032433867454529},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4910457134246826},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.47537508606910706},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.467937171459198},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4132421910762787},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3681596517562866},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.34194082021713257},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17770618200302124}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7752188444137573},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.6492279171943665},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5199043154716492},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.5120354294776917},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.50905841588974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5032433867454529},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4910457134246826},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.47537508606910706},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.467937171459198},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4132421910762787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3681596517562866},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34194082021713257},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17770618200302124},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2309.05927","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.05927","pdf_url":"https://arxiv.org/pdf/2309.05927","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2309.05927","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2309.05927","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2309.05927","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.05927","pdf_url":"https://arxiv.org/pdf/2309.05927","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386721612.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W73545470","https://openalex.org/W4224266612","https://openalex.org/W2383394264","https://openalex.org/W4320153225","https://openalex.org/W4293261942","https://openalex.org/W3125968744","https://openalex.org/W2167701463","https://openalex.org/W2110287964","https://openalex.org/W4307407935","https://openalex.org/W649759291"],"abstract_inverted_index":{"Leveraging":[0],"multimodal":[1,19,125],"information":[2,126],"from":[3,32],"biosignals":[4,20,67],"is":[5,182,202],"vital":[6],"for":[7,83],"building":[8],"a":[9,55,74,79,108,153],"comprehensive":[10],"representation":[11,65],"of":[12,49,66,88,94,143,156,166],"people's":[13],"physical":[14],"and":[15,28,91,129,137,146],"mental":[16],"states.":[17],"However,":[18],"often":[21],"exhibit":[22],"substantial":[23],"distributional":[24,51],"shifts":[25],"between":[26],"pretraining":[27,45,110],"inference":[29],"datasets,":[30],"stemming":[31],"changes":[33],"in":[34,39,46,68,116,169,184,198],"task":[35],"specification":[36],"or":[37,192],"variations":[38],"modality":[40,185,190],"compositions.":[41],"To":[42,96],"achieve":[43],"effective":[44],"the":[47,64,69,89,98,117,173],"presence":[48],"potential":[50],"shifts,":[52],"we":[53,105,177],"propose":[54],"frequency-aware":[56,75],"masked":[57,114],"autoencoder":[58],"($\\texttt{bio}$FAME)":[59],"that":[60,112,179],"learns":[61],"to":[62,134],"parameterize":[63],"frequency":[70,99],"space.":[71,119],"$\\texttt{bio}$FAME":[72],"incorporates":[73],"transformer,":[76],"which":[77],"leverages":[78],"fixed-size":[80],"Fourier-based":[81],"operator":[82],"global":[84],"token":[85],"mixing,":[86],"independent":[87],"length":[90],"sampling":[92],"rate":[93],"inputs.":[95],"maintain":[97],"components":[100],"within":[101],"each":[102],"input":[103,144],"channel,":[104],"further":[106],"employ":[107],"frequency-maintain":[109],"strategy":[111],"performs":[113],"autoencoding":[115],"latent":[118],"The":[120],"resulting":[121],"architecture":[122,181],"effectively":[123],"utilizes":[124],"during":[127],"pretraining,":[128],"can":[130],"be":[131],"seamlessly":[132],"adapted":[133],"diverse":[135,154],"tasks":[136],"modalities":[138],"at":[139,204],"test":[140],"time,":[141],"regardless":[142],"size":[145],"order.":[147],"We":[148],"evaluated":[149],"our":[150,180],"approach":[151],"on":[152,159],"set":[155],"transfer":[157],"experiments":[158],"unimodal":[160],"time":[161],"series,":[162],"achieving":[163],"an":[164],"average":[165],"$\\uparrow$5.5%":[167],"improvement":[168],"classification":[170],"accuracy":[171],"over":[172],"previous":[174],"state-of-the-art.":[175],"Furthermore,":[176],"demonstrated":[178],"robust":[183],"mismatch":[186],"scenarios,":[187],"including":[188],"unpredicted":[189],"dropout":[191],"substitution,":[193],"proving":[194],"its":[195],"practical":[196],"utility":[197],"real-world":[199],"applications.":[200],"Code":[201],"available":[203],"https://github.com/apple/ml-famae":[205],".":[206]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}