{"id":"https://openalex.org/W2951603116","doi":"https://doi.org/10.1145/3292500.3330957","title":"Towards Robust and Discriminative Sequential Data Learning","display_name":"Towards Robust and Discriminative Sequential Data Learning","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2951603116","doi":"https://doi.org/10.1145/3292500.3330957","mag":"2951603116"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330957","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330957","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330957","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330957","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001445783","display_name":"Xiaowei Jia","orcid":"https://orcid.org/0000-0001-8544-5233"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]},{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaowei Jia","raw_affiliation_strings":["University of Minnesota &amp; Adobe Research, Minneapolis, MN, USA"],"affiliations":[{"raw_affiliation_string":"University of Minnesota &amp; Adobe Research, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I1306409833","https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053726259","display_name":"Sheng Li","orcid":"https://orcid.org/0000-0001-7636-3797"},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheng Li","raw_affiliation_strings":["University of Georgia, Athens, GA, USA"],"affiliations":[{"raw_affiliation_string":"University of Georgia, Athens, GA, USA","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024078415","display_name":"Handong Zhao","orcid":"https://orcid.org/0000-0003-3775-2954"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Handong Zhao","raw_affiliation_strings":["Adobe Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100718934","display_name":"Sungchul Kim","orcid":"https://orcid.org/0000-0003-3580-5290"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sungchul Kim","raw_affiliation_strings":["Adobe Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"Adobe Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100645812","display_name":"Vipin Kumar","orcid":"https://orcid.org/0000-0002-9040-2665"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vipin Kumar","raw_affiliation_strings":["University of Minnesota, Minneapolis, MN, USA"],"affiliations":[{"raw_affiliation_string":"University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5001445783"],"corresponding_institution_ids":["https://openalex.org/I130238516","https://openalex.org/I1306409833"],"apc_list":null,"apc_paid":null,"fwci":1.8202,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.88985282,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1665","last_page":"1673"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9994999766349792,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9994999766349792,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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":0.9954000115394592,"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":0.803286075592041},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7073632478713989},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6661263704299927},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6151007413864136},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5862880945205688},{"id":"https://openalex.org/keywords/snapshot","display_name":"Snapshot (computer storage)","score":0.5606905817985535},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5397198796272278},{"id":"https://openalex.org/keywords/sequence-learning","display_name":"Sequence learning","score":0.525963544845581},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4820778965950012},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4473692774772644},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43091148138046265},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37317198514938354}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.803286075592041},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7073632478713989},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6661263704299927},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6151007413864136},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5862880945205688},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.5606905817985535},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5397198796272278},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.525963544845581},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4820778965950012},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4473692774772644},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43091148138046265},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37317198514938354},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330957","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330957","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330957","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3292500.3330957","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3292500.3330957","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3292500.3330957","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7799999713897705}],"awards":[{"id":"https://openalex.org/G1107591779","display_name":null,"funder_award_id":"1838159","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2951603116.pdf","grobid_xml":"https://content.openalex.org/works/W2951603116.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W1902237438","https://openalex.org/W2061873838","https://openalex.org/W2154359981","https://openalex.org/W2163455955","https://openalex.org/W2470673105","https://openalex.org/W2520771490","https://openalex.org/W2557074642","https://openalex.org/W2558799462","https://openalex.org/W2565085003","https://openalex.org/W2606711863","https://openalex.org/W2610321374","https://openalex.org/W2690721124","https://openalex.org/W2737391801","https://openalex.org/W2742706476","https://openalex.org/W2742878349","https://openalex.org/W2766462876","https://openalex.org/W2777353073","https://openalex.org/W2787480667","https://openalex.org/W2787487383","https://openalex.org/W2800046969","https://openalex.org/W2802362396","https://openalex.org/W2803686446","https://openalex.org/W2803718882","https://openalex.org/W2803831897","https://openalex.org/W2809376420","https://openalex.org/W2897950433","https://openalex.org/W2907279123","https://openalex.org/W2944361765","https://openalex.org/W2950577311","https://openalex.org/W2952729433","https://openalex.org/W2953022248","https://openalex.org/W2963249138","https://openalex.org/W2963834268","https://openalex.org/W2964159205","https://openalex.org/W2964253222","https://openalex.org/W2964959375","https://openalex.org/W3098019619","https://openalex.org/W3098276446","https://openalex.org/W3099136959","https://openalex.org/W3103706239","https://openalex.org/W3104839310"],"related_works":["https://openalex.org/W4380075502","https://openalex.org/W4223943233","https://openalex.org/W4312200629","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4380086463","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W3164822677","https://openalex.org/W2889705046"],"abstract_inverted_index":{"The":[0],"last":[1],"decade":[2],"has":[3],"witnessed":[4],"a":[5,19,40,94,115,127,147,163,182],"surge":[6],"of":[7,22,79,114,130,175,184],"interest":[8],"in":[9,74,181],"applying":[10],"deep":[11,28,80],"learning":[12,29,81],"models":[13,30,37,82],"for":[14,152,165],"discovering":[15],"sequential":[16,101,121,153,186],"patterns":[17],"from":[18],"large":[20],"volume":[21],"data.":[23],"Recent":[24],"works":[25],"show":[26],"that":[27,59],"can":[31,49],"be":[32,50],"further":[33],"improved":[34],"by":[35,52,156],"enforcing":[36],"to":[38,62,100,138,161],"learn":[39],"smooth":[41],"output":[42],"distribution":[43],"around":[44],"each":[45],"data":[46,55,88,91,122,154,168],"point.":[47],"This":[48,117],"achieved":[51],"augmenting":[53],"training":[54,68,106,150],"with":[56],"slight":[57],"perturbations":[58],"are":[60,123],"designed":[61],"alter":[63],"model":[64],"outputs.":[65],"Such":[66],"adversarial":[67,105,149],"approaches":[69,107],"have":[70],"shown":[71],"much":[72,135],"success":[73],"improving":[75],"the":[76,103,111,139,173,176],"generalization":[77],"performance":[78],"on":[83,93],"static":[84],"data,":[85,102],"e.g.,":[86],"transaction":[87],"or":[89],"image":[90],"captured":[92],"single":[95],"snapshot.":[96],"However,":[97],"when":[98,158],"applied":[99],"standard":[104],"cannot":[108],"fully":[109],"capture":[110],"discriminative":[112],"structure":[113],"sequence.":[116],"is":[118],"because":[119],"real-world":[120,185],"often":[124],"collected":[125],"over":[126,179],"long":[128],"period":[129],"time":[131],"and":[132,159],"may":[133],"include":[134],"irrelevant":[136],"information":[137],"classification":[140,155],"task.":[141],"To":[142],"this":[143],"end,":[144],"we":[145,171],"develop":[146],"novel":[148],"approach":[151],"investigating":[157],"how":[160],"perturb":[162],"sequence":[164],"an":[166],"effective":[167],"augmentation.":[169],"Finally,":[170],"demonstrate":[172],"superiority":[174],"proposed":[177],"method":[178],"baselines":[180],"diversity":[183],"datasets.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
