{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108022"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108022","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"HiBi: A hierarchical bigram model for associative learning","abstract":"There has been a shift of attention in the AI research where people gradually abandon traditional statistical models in favor of deep neural architectures. While effective in learning input-output mappings from two arbitrary distributions, the complex nature of neural models makes them hard to interpret. In this thesis, we introduce a more interpretable hierarchical bigram (HiBi) model, which is extended based on the simple bigram language model. It contains a few components inspired by theories of human cognition, and has been shown through experiments to be effective in learning meaningful representation from sequential inputs without any labeling. We hope that HiBi could be a starting point to develop more complex cognitive models that are both interpretable and effective for representation learning.","abstract_html":"There has been a shift of attention in the AI research where people gradually abandon traditional statistical models in favor of deep neural architectures. While effective in learning input-output mappings from two arbitrary distributions, the complex nature of neural models makes them hard to interpret. In this thesis, we introduce a more interpretable hierarchical bigram (HiBi) model, which is extended based on the simple bigram language model. It contains a few components inspired by theories of human cognition, and has been shown through experiments to be effective in learning meaningful representation from sequential inputs without any labeling. We hope that HiBi could be a starting point to develop more complex cognitive models that are both interpretable and effective for representation learning.","abstract_has_math":false,"creators":["Wang, Xiaoyan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, Chengxiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:57:59Z","date_published":"2020-08-26T21:57:59Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Natural language processing","Cognitive models"],"languages":["en"],"rights":["Copyright 2020 Xiaoyan Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108022","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, Chengxiang"]},{"key":"dc:creator","label":"Author","values":["Wang, Xiaoyan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:57:59Z","2020-05-11","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Natural language processing","Cognitive models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Xiaoyan Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108022"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["There has been a shift of attention in the AI research where people gradually abandon traditional statistical models in favor of deep neural architectures. While effective in learning input-output mappings from two arbitrary distributions, the complex nature of neural models makes them hard to interpret. In this thesis, we introduce a more interpretable hierarchical bigram (HiBi) model, which is extended based on the simple bigram language model. It contains a few components inspired by theories of human cognition, and has been shown through experiments to be effective in learning meaningful representation from sequential inputs without any labeling. We hope that HiBi could be a starting point to develop more complex cognitive models that are both interpretable and effective for representation learning.","Submission original under an indefinite embargo labeled 'Open Access'. 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In this thesis, we introduce a more interpretable hierarchical bigram (HiBi) model, which is extended based on the simple bigram language model. It contains a few components inspired by theories of human cognition, and has been shown through experiments to be effective in learning meaningful representation from sequential inputs without any labeling. We hope that HiBi could be a starting point to develop more complex cognitive models that are both interpretable and effective for representation learning.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Xiaoyan Wang, accepted the attached license on 2020-05-08 at 17:13.","The student, Xiaoyan Wang, submitted this Thesis for approval on 2020-05-08 at 17:20.","This Thesis was approved for publication on 2020-05-11 at 15:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15304 on 2020-08-25 at 17:13:40","Made available in DSpace on 2020-08-26T21:57:59Z (GMT). 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