{"id":"https://openalex.org/W3023457900","doi":"https://doi.org/10.1109/ciss48834.2020.1570617364","title":"Performance Analysis for Tensor-Train Decomposition to Deep Neural Network Based Vector-to-Vector Regression","display_name":"Performance Analysis for Tensor-Train Decomposition to Deep Neural Network Based Vector-to-Vector Regression","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3023457900","doi":"https://doi.org/10.1109/ciss48834.2020.1570617364","mag":"3023457900"},"language":"en","primary_location":{"id":"doi:10.1109/ciss48834.2020.1570617364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss48834.2020.1570617364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042520842","display_name":"Jun Qi","orcid":"https://orcid.org/0000-0001-7533-2630"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jun Qi","raw_affiliation_strings":["Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001706958","display_name":"Xiaoli Ma","orcid":"https://orcid.org/0000-0002-3076-2589"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoli Ma","raw_affiliation_strings":["Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066868860","display_name":"Chin\u2010Hui Lee","orcid":"https://orcid.org/0000-0002-1892-2551"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chin-Hui Lee","raw_affiliation_strings":["Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066595711","display_name":"Jun Du","orcid":"https://orcid.org/0000-0002-2387-0389"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Du","raw_affiliation_strings":["Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079659476","display_name":"Sabato Marco Siniscalchi","orcid":"https://orcid.org/0000-0002-0770-0507"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sabato Marco Siniscalchi","raw_affiliation_strings":["Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5042520842"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.8407,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67875,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.980400025844574,"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/T13650","display_name":"Computational Physics and Python Applications","score":0.953000009059906,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6093834042549133},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5718957185745239},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5590178370475769},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5582587718963623},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5416808724403381},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"Tensor decomposition","score":0.49123185873031616},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.48960381746292114},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.440583735704422},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4227867126464844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2533874809741974},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13815435767173767}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6093834042549133},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5718957185745239},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5590178370475769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5582587718963623},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5416808724403381},{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.49123185873031616},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.48960381746292114},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.440583735704422},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4227867126464844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2533874809741974},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13815435767173767},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ciss48834.2020.1570617364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ciss48834.2020.1570617364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.unipa.it:10447/636624","is_oa":false,"landing_page_url":"https://hdl.handle.net/10447/636624","pdf_url":null,"source":{"id":"https://openalex.org/S4306401065","display_name":"Nova Science Publishers (Nova Science Publishers, Inc.)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/bookPart"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1798945469","https://openalex.org/W1993482030","https://openalex.org/W2007339694","https://openalex.org/W2039240409","https://openalex.org/W2044893557","https://openalex.org/W2078528584","https://openalex.org/W2146337213","https://openalex.org/W2155195660","https://openalex.org/W2182396527","https://openalex.org/W2753969545","https://openalex.org/W2804589149","https://openalex.org/W2886067286","https://openalex.org/W2899874324","https://openalex.org/W2900103278","https://openalex.org/W2949804919","https://openalex.org/W2963248893","https://openalex.org/W2963834323","https://openalex.org/W2969649301","https://openalex.org/W2970330753","https://openalex.org/W4289293816","https://openalex.org/W4289382653","https://openalex.org/W4297813530","https://openalex.org/W4300558631","https://openalex.org/W6638060716","https://openalex.org/W6678296350","https://openalex.org/W6681686951","https://openalex.org/W6682891771","https://openalex.org/W6752009368","https://openalex.org/W6753918066","https://openalex.org/W6755487116"],"related_works":["https://openalex.org/W4379256054","https://openalex.org/W2093953080","https://openalex.org/W2911706637","https://openalex.org/W47805180","https://openalex.org/W2963838862","https://openalex.org/W3015641590","https://openalex.org/W3216281372","https://openalex.org/W2987657992","https://openalex.org/W2949531434","https://openalex.org/W4286927328"],"abstract_inverted_index":{"This":[0],"work":[1],"focuses":[2],"on":[3,64,82,96],"a":[4,28,34],"performance":[5,99],"analysis":[6],"of":[7,93],"tensor-train":[8,25,49],"decomposition":[9],"applied":[10],"to":[11],"the":[12,48,53,61,65,83,87,91],"deep":[13],"neural":[14,76],"network":[15],"(DNN)":[16],"based":[17,30],"vector-to-vector":[18,31],"regression.":[19],"Tensor-train":[20],"Network":[21],"(TTN),":[22],"obtained":[23],"through":[24],"decomposition,":[26],"converts":[27],"DNN":[29,46,81],"regression":[32],"into":[33],"tensor-to-vector":[35],"mapping":[36],"with":[37,47],"fewer":[38],"parameters.":[39],"We":[40,73],"can":[41,56,70],"therefore":[42],"build":[43],"an":[44,79],"over-parametrized":[45,80],"representation":[50],"such":[51],"that":[52],"optimization":[54],"error":[55],"be":[57,71],"significantly":[58],"reduced,":[59],"while":[60],"upper":[62],"bounds":[63],"approximation":[66],"and":[67,86],"estimation":[68],"errors":[69],"maintained.":[72],"compare":[74],"TTN-based":[75],"architecture":[77],"against":[78],"MNIST":[84],"dataset,":[85],"experimental":[88],"evidence":[89],"demonstrates":[90],"validity":[92],"our":[94,97],"conjectures":[95],"proposed":[98],"bounds.":[100]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
