{"id":"https://openalex.org/W2980731561","doi":"https://doi.org/10.1109/jsait.2020.2991005","title":"Extracting Robust and Accurate Features via a Robust Information Bottleneck","display_name":"Extracting Robust and Accurate Features via a Robust Information Bottleneck","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W2980731561","doi":"https://doi.org/10.1109/jsait.2020.2991005","mag":"2980731561"},"language":"en","primary_location":{"id":"doi:10.1109/jsait.2020.2991005","doi":"https://doi.org/10.1109/jsait.2020.2991005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsait.2020.2991005","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Information Theory","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1910.06893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046067745","display_name":"Ankit Pensia","orcid":"https://orcid.org/0000-0003-3509-1224","observed_orcids":["https://orcid.org/0000-0003-3509-1224"]},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankit Pensia","raw_affiliation_strings":["Department of Computer Sciences, University of Wisconsin\u2013Madison, Madison, USA","University of Wisconsin-Madison, #TAB#"],"raw_orcid":"https://orcid.org/0000-0003-3509-1224","affiliations":[{"raw_affiliation_string":"Department of Computer Sciences, University of Wisconsin\u2013Madison, Madison, USA","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin-Madison, #TAB#","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021502006","display_name":"Varun Jog","orcid":"https://orcid.org/0000-0003-4159-0900","observed_orcids":["https://orcid.org/0000-0003-4159-0900"]},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Varun Jog","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Wisconsin\u2013Madison, Madison, USA","University of Wisconsin-Madison, #TAB#"],"raw_orcid":"https://orcid.org/0000-0003-4159-0900","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Wisconsin\u2013Madison, Madison, USA","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin-Madison, #TAB#","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023368186","display_name":"Po\u2010Ling Loh","orcid":"https://orcid.org/0000-0002-6514-7834","observed_orcids":["https://orcid.org/0000-0002-6514-7834"]},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]},{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Po-Ling Loh","raw_affiliation_strings":["Department of Statistics, Columbia University, New York, USA","Department of Statistics, University of Wisconsin\u2013Madison, Madison, USA","University of Wisconsin-Madison, #TAB#"],"raw_orcid":"https://orcid.org/0000-0002-6514-7834","affiliations":[{"raw_affiliation_string":"Department of Statistics, Columbia University, New York, USA","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Department of Statistics, University of Wisconsin\u2013Madison, Madison, USA","institution_ids":["https://openalex.org/I135310074"]},{"raw_affiliation_string":"University of Wisconsin-Madison, #TAB#","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":1.5025,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.84254117,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":"1","issue":"1","first_page":"131","last_page":"144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.35839998722076416,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.35839998722076416,"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/T10320","display_name":"Neural Networks and Applications","score":0.16750000417232513,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.14169999957084656,"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/robust-feature-extraction","display_name":"robust feature extraction","score":0.8889999985694885},{"id":"https://openalex.org/keywords/information-bottleneck","display_name":"information bottleneck","score":0.9150000214576721},{"id":"https://openalex.org/keywords/fisher-information","display_name":"Fisher information","score":0.6079999804496765},{"id":"https://openalex.org/keywords/accuracy-robustness-trade-off","display_name":"robustness-accuracy trade-off","score":0.7799999713897705},{"id":"https://openalex.org/keywords/mutual-information","display_name":"mutual information","score":0.8999999761581421},{"id":"https://openalex.org/keywords/mini-mental-state-examination","display_name":"Mini-Mental State Examination","score":0.6959999799728394},{"id":"https://openalex.org/keywords/regularization","display_name":"regularization","score":0.5730000138282776},{"id":"https://openalex.org/keywords/variational-bounds","display_name":"variational bounds","score":0.8560000061988831},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"stochastic gradient descent","score":0.6589999794960022},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.6169999837875366},{"id":"https://openalex.org/keywords/robustness-to-disturbances","display_name":"robustness to disturbances","score":0.7260000109672546}],"concepts":[{"id":"https://openalex.org/C60008888","wikidata":"https://www.wikidata.org/wiki/Q6031013","display_name":"Information bottleneck method","level":3,"score":0.8213919401168823},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7971179485321045},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6687038540840149},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6343486905097961},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5230063796043396},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5071022510528564},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.500457763671875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45576557517051697},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39254969358444214},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.348408043384552},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3354722559452057},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32205629348754883},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22834649682044983},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.11293193697929382},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/jsait.2020.2991005","doi":"https://doi.org/10.1109/jsait.2020.2991005","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsait.2020.2991005","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","jufo-1","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Information Theory","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1910.06893","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.06893","pdf_url":"https://arxiv.org/pdf/1910.06893","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":"mag:2980731561","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1910.06893","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1910.06893","doi":"https://doi.org/10.48550/arxiv.1910.06893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1910.06893","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1910.06893","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1910.06893","pdf_url":"https://arxiv.org/pdf/1910.06893","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":[{"id":"https://openalex.org/G1184955639","display_name":null,"funder_award_id":"DMS-1749857","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G3080512194","display_name":"TRIPODS: Institute for Foundations of Data Science","funder_award_id":"1740707","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3440342489","display_name":"CAREER: Something Old, Something New: Robust Statistics in the 21st Century","funder_award_id":"1749857","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7919732041","display_name":"EAGER: Developing a Theory for Function Optimization on Graphs Using Local Information","funder_award_id":"1841190","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8519814234","display_name":"CIF: Small: Bridging the Inequality Gap","funder_award_id":"1907786","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"},{"id":"https://openalex.org/F4320306555","display_name":"Wisconsin Alumni Research Foundation","ror":"https://ror.org/00hwxbz16"},{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320334164","display_name":"Simons Institute for the Theory of Computing, University of California Berkeley","ror":null},{"id":"https://openalex.org/F4320335353","display_name":"National Science Foundation of Sri Lanka","ror":"https://ror.org/010xaa060"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2980731561.pdf","grobid_xml":"https://content.openalex.org/works/W2980731561.grobid-xml"},"referenced_works_count":87,"referenced_works":["https://openalex.org/W145805811","https://openalex.org/W391578156","https://openalex.org/W1503425191","https://openalex.org/W1673923490","https://openalex.org/W1686946872","https://openalex.org/W1923001393","https://openalex.org/W1945616565","https://openalex.org/W1959608418","https://openalex.org/W1965042169","https://openalex.org/W1974454156","https://openalex.org/W1994534580","https://openalex.org/W2002867407","https://openalex.org/W2041880317","https://openalex.org/W2043371182","https://openalex.org/W2051868869","https://openalex.org/W2060314721","https://openalex.org/W2096516049","https://openalex.org/W2098145033","https://openalex.org/W2099111195","https://openalex.org/W2099294566","https://openalex.org/W2117533361","https://openalex.org/W2122759946","https://openalex.org/W2123469175","https://openalex.org/W2133564696","https://openalex.org/W2141761557","https://openalex.org/W2144094762","https://openalex.org/W2152722485","https://openalex.org/W2154221712","https://openalex.org/W2156163116","https://openalex.org/W2163605009","https://openalex.org/W2170503197","https://openalex.org/W2171750587","https://openalex.org/W2171801319","https://openalex.org/W2210387432","https://openalex.org/W2257979135","https://openalex.org/W2301217285","https://openalex.org/W2398870399","https://openalex.org/W2473418344","https://openalex.org/W2543927648","https://openalex.org/W2557579533","https://openalex.org/W2593634001","https://openalex.org/W2594475271","https://openalex.org/W2640329709","https://openalex.org/W2767075075","https://openalex.org/W2773633221","https://openalex.org/W2773726006","https://openalex.org/W2786163515","https://openalex.org/W2787480667","https://openalex.org/W2787708942","https://openalex.org/W2792772107","https://openalex.org/W2799107510","https://openalex.org/W2809994596","https://openalex.org/W2810408730","https://openalex.org/W2883108656","https://openalex.org/W2889923254","https://openalex.org/W2891262038","https://openalex.org/W2897355816","https://openalex.org/W2897652134","https://openalex.org/W2902227449","https://openalex.org/W2911634294","https://openalex.org/W2912008365","https://openalex.org/W2913266441","https://openalex.org/W2921173985","https://openalex.org/W2927580826","https://openalex.org/W2942630857","https://openalex.org/W2945007002","https://openalex.org/W2945033152","https://openalex.org/W2945090955","https://openalex.org/W2947518183","https://openalex.org/W2950181023","https://openalex.org/W2951004968","https://openalex.org/W2951735139","https://openalex.org/W2963207607","https://openalex.org/W2963539647","https://openalex.org/W2964153729","https://openalex.org/W2964160479","https://openalex.org/W2964192498","https://openalex.org/W2964253222","https://openalex.org/W2964308564","https://openalex.org/W2970115835","https://openalex.org/W2975007897","https://openalex.org/W2989696285","https://openalex.org/W3013104126","https://openalex.org/W3014590323","https://openalex.org/W4205125306","https://openalex.org/W4241772732","https://openalex.org/W4289147935"],"related_works":["https://openalex.org/W2109857912","https://openalex.org/W3214605888","https://openalex.org/W4394939129","https://openalex.org/W2947731685","https://openalex.org/W3158076588","https://openalex.org/W7097299512","https://openalex.org/W3177538078","https://openalex.org/W3134837785","https://openalex.org/W4307473403","https://openalex.org/W7140258735"],"abstract_inverted_index":{"We":[0,63,105,136],"propose":[1,138],"a":[2,17,103],"novel":[3],"strategy":[4],"for":[5,140,168],"extracting":[6],"features":[7,54,72,128],"in":[8,26,133,150,162],"supervised":[9],"learning":[10],"that":[11,46,124,175],"can":[12,90],"be":[13,56,159],"used":[14],"to":[15,23,55,73,109,187],"construct":[16],"classifier":[18],"which":[19,157],"is":[20,76],"more":[21],"robust":[22,111,127,146],"small":[24,57],"perturbations":[25],"the":[27,34,37,48,52,61,68,71,86,94,116,125,145,176],"input":[28],"space.":[29],"Our":[30,165],"method":[31],"builds":[32],"upon":[33],"idea":[35],"of":[36,51,70,97],"information":[38,50,81,112,147],"bottleneck,":[39],"by":[40,60],"introducing":[41],"an":[42],"additional":[43],"penalty":[44],"term":[45],"encourages":[47],"Fisher":[49],"extracted":[53],"when":[58,101,115],"parametrized":[59],"inputs.":[62],"present":[64],"two":[65],"formulations":[66,114],"where":[67],"relevance":[69],"output":[74],"labels":[75],"measured":[77],"using":[78,153],"either":[79],"mutual":[80],"or":[82],"MMSE.":[83],"By":[84],"tuning":[85],"regularization":[87],"parameter,":[88],"we":[89],"explicitly":[91],"trade":[92],"off":[93],"opposing":[95],"desiderata":[96],"robustness":[98,186],"and":[99,118,170],"accuracy":[100],"constructing":[102],"classifier.":[104],"derive":[106],"optimal":[107],"solutions":[108],"both":[110],"bottleneck":[113,148],"inputs":[117],"outputs":[119],"are":[120,129],"jointly":[121,131],"Gaussian,":[122],"proving":[123],"optimally":[126],"also":[130,137],"Gaussian":[132],"this":[134],"setting.":[135],"methods":[139,180],"optimizing":[141],"variational":[142],"bounds":[143],"on":[144],"objectives":[149],"general":[151],"settings":[152],"stochastic":[154],"gradient":[155],"descent,":[156],"may":[158],"implemented":[160],"efficiently":[161],"neural":[163],"networks.":[164],"experimental":[166],"results":[167],"synthetic":[169],"real":[171],"data":[172],"sets":[173],"show":[174],"proposed":[177],"feature":[178],"extraction":[179],"indeed":[181],"produce":[182],"classifiers":[183],"with":[184],"increased":[185],"perturbations.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2019-10-25T00:00:00"}