{"id":"https://openalex.org/W2963255584","doi":"https://doi.org/10.48550/arxiv.1606.04443","title":"A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification","display_name":"A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification","publication_year":2016,"publication_date":"2016-06-14","ids":{"openalex":"https://openalex.org/W2963255584","doi":"https://doi.org/10.48550/arxiv.1606.04443","mag":"2963255584"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1606.04443","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1606.04443","pdf_url":"https://arxiv.org/pdf/1606.04443","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,"listed_in":[],"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":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1606.04443","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069059871","display_name":"Steven Cheng-Xian Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li, Steven Cheng-Xian","raw_affiliation_strings":["College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA;","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023142437","display_name":"Benjamin M. Marlin","orcid":"https://orcid.org/0000-0002-2626-3410","observed_orcids":["https://orcid.org/0000-0002-2626-3410"]},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marlin, Benjamin","raw_affiliation_strings":["College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA;","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":57,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":null,"first_page":"1812","last_page":"1820"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.8798999786376953,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.8798999786376953,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.1111999973654747,"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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/irregularly-sampled-time-series","display_name":"irregularly sampled time series","score":0.9480000138282776},{"id":"https://openalex.org/keywords/sparse-time-series","display_name":"sparse time series","score":0.7070000171661377},{"id":"https://openalex.org/keywords/uncertainty-aware-classification","display_name":"uncertainty-aware classification","score":0.9520000219345093},{"id":"https://openalex.org/keywords/end-to-end-learning","display_name":"end-to-end learning","score":0.6959999799728394},{"id":"https://openalex.org/keywords/black-box-classifiers","display_name":"black-box classifiers","score":0.6240000128746033},{"id":"https://openalex.org/keywords/scalable-computing","display_name":"scalable computing","score":0.5009999871253967},{"id":"https://openalex.org/keywords/time-series-classification","display_name":"time series classification","score":0.7519999742507935}],"concepts":[{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.7008244395256042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6969524621963501},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5088282227516174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5052235722541809},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4771040081977844},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.45987409353256226},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4531264305114746},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4463839530944824},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4444204270839691},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.43534910678863525},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43499985337257385},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.4285928010940552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3696812391281128},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.31866446137428284},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18341830372810364},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1606.04443","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1606.04443","pdf_url":"https://arxiv.org/pdf/1606.04443","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,"listed_in":[],"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1606.04443","doi":"https://doi.org/10.48550/arxiv.1606.04443","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1606.04443","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,"listed_in":[],"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"},{"id":"mag:2963255584","doi":null,"is_oa":false,"landing_page_url":"https://papers.nips.cc/paper/6475-a-scalable-end-to-end-gaussian-process-adapter-for-irregularly-sampled-time-series-classification.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1606.04443","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1606.04443","pdf_url":"https://arxiv.org/pdf/1606.04443","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,"listed_in":[],"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":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W48400444","https://openalex.org/W1506342804","https://openalex.org/W1522703524","https://openalex.org/W1702387805","https://openalex.org/W1746819321","https://openalex.org/W1959608418","https://openalex.org/W1976798627","https://openalex.org/W1986316936","https://openalex.org/W1997454815","https://openalex.org/W2000032310","https://openalex.org/W2040870891","https://openalex.org/W2084813411","https://openalex.org/W2086824111","https://openalex.org/W2086972503","https://openalex.org/W2087778158","https://openalex.org/W2088748973","https://openalex.org/W2106981782","https://openalex.org/W2125849100","https://openalex.org/W2130942839","https://openalex.org/W2134557905","https://openalex.org/W2147780311","https://openalex.org/W2157494358","https://openalex.org/W2400373170","https://openalex.org/W2949644579"],"related_works":["https://openalex.org/W2400373170","https://openalex.org/W3043013016","https://openalex.org/W2735861449","https://openalex.org/W2956051102","https://openalex.org/W3210522174","https://openalex.org/W7095683088","https://openalex.org/W7140162748","https://openalex.org/W7167854887","https://openalex.org/W7213887542","https://openalex.org/W3195564206"],"abstract_inverted_index":{"We":[0,89],"present":[1],"a":[2,61,123],"general":[3],"framework":[4,58,105],"for":[5],"classification":[6,42,57],"of":[7,15,27,125],"sparse":[8],"and":[9,106,111],"irregularly-sampled":[10],"time":[11,17,78],"series.":[12],"The":[13],"properties":[14],"such":[16],"series":[18,79],"can":[19,74],"result":[20],"in":[21,120],"substantial":[22],"uncertainty":[23],"about":[24],"the":[25,28,34,69,95,101,107,116],"values":[26],"underlying":[29],"temporal":[30],"processes,":[31],"while":[32],"making":[33],"data":[35,80],"difficult":[36],"to":[37,67,81,92,113],"deal":[38],"with":[39,122],"using":[40,86,128],"standard":[41],"methods":[43],"that":[44,73],"assume":[45],"fixed-dimensional":[46],"feature":[47],"spaces.":[48],"To":[49],"address":[50],"these":[51],"challenges,":[52],"we":[53,65],"propose":[54],"an":[55],"uncertainty-aware":[56],"based":[59,98],"on":[60,99],"special":[62],"computational":[63],"layer":[64],"refer":[66],"as":[68],"Gaussian":[70,117],"process":[71,118],"adapter":[72,119],"connect":[75],"irregularly":[76],"sampled":[77],"any":[82],"black-box":[83],"classifier":[84],"learnable":[85],"gradient":[87],"descent.":[88],"show":[90],"how":[91,112],"scale":[93],"up":[94],"required":[96],"computations":[97],"combining":[100],"structured":[102],"kernel":[103],"interpolation":[104],"Lanczos":[108],"approximation":[109],"method,":[110],"discriminatively":[114],"train":[115],"combination":[121],"number":[124],"classifiers":[126],"end-to-end":[127],"backpropagation.":[129]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":4}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2019-07-30T00:00:00"}