{"id":"https://openalex.org/W7164858261","doi":"https://doi.org/10.48550/arxiv.2606.13970","title":"An Attention-based Model for Robust Forecasting with Missing Modality","display_name":"An Attention-based Model for Robust Forecasting with Missing Modality","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164858261","doi":"https://doi.org/10.48550/arxiv.2606.13970"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13970","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13970","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.13970","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138667518","display_name":"Zhitian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhitian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062367314","display_name":"Wenjie Zi","orcid":"https://orcid.org/0009-0001-1974-4822"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zi, Wenjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050835612","display_name":"Yunduz Rakhmangulova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rakhmangulova, Yunduz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017858754","display_name":"Saghar Irandoust","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Irandoust, Saghar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060135722","display_name":"Hossein Hajimirsadeghi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hajimirsadeghi, Hossein","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5018335104","display_name":"T Durand","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Durand, Thibaut","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":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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1890999972820282,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1890999972820282,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.13830000162124634,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.07900000363588333,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/modalities","display_name":"Modalities","score":0.8447999954223633},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6862999796867371},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.6717000007629395},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.6003000140190125},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5903000235557556},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5623999834060669},{"id":"https://openalex.org/keywords/robotics","display_name":"Robotics","score":0.4636000096797943},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4586000144481659},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.448199987411499}],"concepts":[{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.8447999954223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7591999769210815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7161999940872192},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6862999796867371},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.6717000007629395},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5903000235557556},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5756999850273132},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5623999834060669},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.4636000096797943},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4586000144481659},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C60692881","wikidata":"https://www.wikidata.org/wiki/Q584529","display_name":"Humanoid robot","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C145460709","wikidata":"https://www.wikidata.org/wiki/Q859951","display_name":"Human\u2013robot interaction","level":3,"score":0.25699999928474426},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13970","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13970","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":"doi:10.48550/arxiv.2606.13970","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13970","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7213613986968994}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"with":[1,20,38,124],"missing":[2,77,125],"modalities":[3,37,50,78,112,126],"is":[4,86,167],"a":[5,39,71,89,96,105,129],"fundamental":[6],"challenge":[7],"in":[8,18,61],"multimodal":[9,30,45,72,141,171],"robot":[10,145,152],"learning,":[11],"as":[12,88],"real-world":[13],"robotic":[14,62],"systems":[15],"often":[16],"operate":[17],"environments":[19],"incomplete":[21,164],"sensor":[22],"data.":[23],"Attention-based":[24],"models":[25,46],"are":[26,51,113],"appealing":[27],"for":[28],"processing":[29],"data":[31,165],"because":[32],"they":[33],"can":[34,121],"handle":[35,76],"multiple":[36],"single":[40],"backbone":[41],"network.":[42],"However,":[43],"most":[44],"assume":[47],"that":[48,99,117,158],"all":[49,133],"available":[52],"during":[53,79],"both":[54,80],"training":[55,81],"and":[56,64,82,94,151,166],"inference,":[57],"limiting":[58],"their":[59],"applicability":[60],"perception":[63],"decision-making.":[65],"In":[66],"this":[67],"paper,":[68],"we":[69],"introduce":[70],"model":[73,85,120,160],"designed":[74],"to":[75,103,169],"inference.":[83],"The":[84],"formulated":[87],"conditional":[90],"variational":[91],"autoencoder":[92],"(CVAE)":[93],"incorporates":[95],"transformer-based":[97],"architecture":[98],"leverages":[100],"attention":[101],"mechanisms":[102],"learn":[104],"unified,":[106],"fixed-dimensional":[107],"representation,":[108],"even":[109],"when":[110],"some":[111],"missing.":[114],"We":[115,135],"show":[116],"our":[118,137,159],"proposed":[119],"be":[122],"trained":[123],"while":[127],"approximating":[128],"robust":[130],"representation":[131],"of":[132],"modalities.":[134],"evaluate":[136],"approach":[138],"on":[139],"five":[140],"datasets":[142],"across":[143],"two":[144],"learning":[146],"tasks:":[147],"human":[148],"trajectory":[149],"prediction":[150],"manipulation":[153],"forecasting.":[154],"Experimental":[155],"results":[156],"demonstrate":[157],"effectively":[161],"learns":[162],"from":[163],"superior":[168],"prior":[170],"fusion":[172],"approaches.":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-16T00:00:00"}