{"id":"https://openalex.org/W4408165311","doi":"https://doi.org/10.1016/j.neunet.2025.107364","title":"Deep Huber quantile regression networks","display_name":"Deep Huber quantile regression networks","publication_year":2025,"publication_date":"2025-03-05","ids":{"openalex":"https://openalex.org/W4408165311","doi":"https://doi.org/10.1016/j.neunet.2025.107364","pmid":"https://pubmed.ncbi.nlm.nih.gov/40112635"},"language":"en","primary_location":{"id":"doi:10.1016/j.neunet.2025.107364","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2025.107364","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.neunet.2025.107364","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003329348","display_name":"Hristos Tyralis","orcid":"https://orcid.org/0000-0002-8932-4997"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Hristos Tyralis","raw_affiliation_strings":["Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, Zografou 157 80, Greece; Construction Agency, Hellenic Air Force, Mesogion Avenue 227-231, Cholargos 15 561, Greece. Electronic address: hristos@itia.ntua.gr"],"affiliations":[{"raw_affiliation_string":"Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, Zografou 157 80, Greece; Construction Agency, Hellenic Air Force, Mesogion Avenue 227-231, Cholargos 15 561, Greece. Electronic address: hristos@itia.ntua.gr","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045992049","display_name":"Georgia Papacharalampous","orcid":"https://orcid.org/0000-0001-5446-954X"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Georgia Papacharalampous","raw_affiliation_strings":["Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, Zografou 157 80, Greece"],"affiliations":[{"raw_affiliation_string":"Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, Zografou 157 80, Greece","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067906219","display_name":"Nilay Do\u011fulu","orcid":"https://orcid.org/0000-0003-4229-2788"},"institutions":[{"id":"https://openalex.org/I1335690228","display_name":"World Meteorological Organization","ror":"https://ror.org/011pjwf87","country_code":"CH","type":"government","lineage":["https://openalex.org/I1335690228"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Nilay Dogulu","raw_affiliation_strings":["Hydrology, Water Resources and Cryosphere Branch, World Meteorological Organisation (WMO), Geneva, Switzerland"],"affiliations":[{"raw_affiliation_string":"Hydrology, Water Resources and Cryosphere Branch, World Meteorological Organisation (WMO), Geneva, Switzerland","institution_ids":["https://openalex.org/I1335690228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005362656","display_name":"Kwok Pan Chun","orcid":"https://orcid.org/0000-0001-9873-6240"},"institutions":[{"id":"https://openalex.org/I178535277","display_name":"University of the West of England","ror":"https://ror.org/02nwg5t34","country_code":"GB","type":"education","lineage":["https://openalex.org/I178535277"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kwok P. Chun","raw_affiliation_strings":["Department of Geography and Environmental Management, University of the West of England, Bristol, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Management, University of the West of England, Bristol, United Kingdom","institution_ids":["https://openalex.org/I178535277"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5003329348"],"corresponding_institution_ids":["https://openalex.org/I174458059"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":14.2363,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.98929023,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"187","issue":null,"first_page":"107364","last_page":"107364"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/T10320","display_name":"Neural Networks and Applications","score":0.987500011920929,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9872000217437744,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.6608002185821533},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5724027752876282},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5264939665794373},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5067546963691711},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4699528217315674},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4479033946990967},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.43778663873672485},{"id":"https://openalex.org/keywords/cross-sectional-regression","display_name":"Cross-sectional regression","score":0.4301043450832367},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.42990097403526306},{"id":"https://openalex.org/keywords/quantile","display_name":"Quantile","score":0.41553518176078796},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31306979060173035},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3055351972579956},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.17124950885772705}],"concepts":[{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.6608002185821533},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5724027752876282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5264939665794373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5067546963691711},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4699528217315674},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4479033946990967},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.43778663873672485},{"id":"https://openalex.org/C90157343","wikidata":"https://www.wikidata.org/wiki/Q5188196","display_name":"Cross-sectional regression","level":4,"score":0.4301043450832367},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.42990097403526306},{"id":"https://openalex.org/C118671147","wikidata":"https://www.wikidata.org/wiki/Q578714","display_name":"Quantile","level":2,"score":0.41553518176078796},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31306979060173035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3055351972579956},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.17124950885772705}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012044","descriptor_name":"Regression Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012044","descriptor_name":"Regression Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1016/j.neunet.2025.107364","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2025.107364","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},{"id":"pmid:40112635","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40112635","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural networks : the official journal of the International Neural Network Society","raw_type":null}],"best_oa_location":{"id":"doi:10.1016/j.neunet.2025.107364","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2025.107364","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":104,"referenced_works":["https://openalex.org/W1573647811","https://openalex.org/W1678356000","https://openalex.org/W1709476246","https://openalex.org/W1892664186","https://openalex.org/W1900727905","https://openalex.org/W1998274472","https://openalex.org/W2004803051","https://openalex.org/W2013182691","https://openalex.org/W2016043834","https://openalex.org/W2025720061","https://openalex.org/W2028399164","https://openalex.org/W2030702674","https://openalex.org/W2042590947","https://openalex.org/W2044551864","https://openalex.org/W2046033161","https://openalex.org/W2066888021","https://openalex.org/W2075965721","https://openalex.org/W2076063813","https://openalex.org/W2080787071","https://openalex.org/W2095705004","https://openalex.org/W2100483895","https://openalex.org/W2101234009","https://openalex.org/W2130700086","https://openalex.org/W2136658108","https://openalex.org/W2137401704","https://openalex.org/W2139786505","https://openalex.org/W2154065358","https://openalex.org/W2170557696","https://openalex.org/W2171666055","https://openalex.org/W2242464395","https://openalex.org/W2275088575","https://openalex.org/W2289748525","https://openalex.org/W2319559145","https://openalex.org/W2331199375","https://openalex.org/W2342249984","https://openalex.org/W2346600303","https://openalex.org/W2510538700","https://openalex.org/W2582743722","https://openalex.org/W2585908831","https://openalex.org/W2597704325","https://openalex.org/W2788650779","https://openalex.org/W2789710473","https://openalex.org/W2794685287","https://openalex.org/W2796293253","https://openalex.org/W2807414627","https://openalex.org/W2897980676","https://openalex.org/W2902722330","https://openalex.org/W2919115771","https://openalex.org/W2963870508","https://openalex.org/W2971675004","https://openalex.org/W2990427812","https://openalex.org/W2996464143","https://openalex.org/W3014596384","https://openalex.org/W3035965352","https://openalex.org/W3099878876","https://openalex.org/W3102100346","https://openalex.org/W3102511045","https://openalex.org/W3134774296","https://openalex.org/W3149414568","https://openalex.org/W3150635270","https://openalex.org/W3155346408","https://openalex.org/W3164731060","https://openalex.org/W3166111593","https://openalex.org/W3188716968","https://openalex.org/W3193067890","https://openalex.org/W3197050061","https://openalex.org/W3210061498","https://openalex.org/W3210146484","https://openalex.org/W3215616542","https://openalex.org/W4233782578","https://openalex.org/W4237576003","https://openalex.org/W4241653265","https://openalex.org/W4243018098","https://openalex.org/W4301106181","https://openalex.org/W4301400955","https://openalex.org/W4308016146","https://openalex.org/W4311763273","https://openalex.org/W4313650490","https://openalex.org/W4366392595","https://openalex.org/W4375860757","https://openalex.org/W4392896772","https://openalex.org/W4399583783","https://openalex.org/W4399637561","https://openalex.org/W4400673012","https://openalex.org/W6631190155","https://openalex.org/W6634147026","https://openalex.org/W6658174601","https://openalex.org/W6667716748","https://openalex.org/W6674330103","https://openalex.org/W6675354045","https://openalex.org/W6682585583","https://openalex.org/W6713134421","https://openalex.org/W6745410505","https://openalex.org/W6755539988","https://openalex.org/W6760520499","https://openalex.org/W6767624591","https://openalex.org/W6783496026","https://openalex.org/W6785335769","https://openalex.org/W6796670083","https://openalex.org/W6809741055","https://openalex.org/W6809800958","https://openalex.org/W6862286353","https://openalex.org/W6869371587","https://openalex.org/W6870228664"],"related_works":["https://openalex.org/W4206511378","https://openalex.org/W4206618949","https://openalex.org/W2468831641","https://openalex.org/W4289356671","https://openalex.org/W4225947116","https://openalex.org/W2034959125","https://openalex.org/W2374894797","https://openalex.org/W2080727847","https://openalex.org/W2091740012","https://openalex.org/W2023475031"],"abstract_inverted_index":{"Typical":[0],"machine":[1],"learning":[2,60],"regression":[3,70,83],"applications":[4,185],"aim":[5],"to":[6,51,120,147,186],"report":[7],"the":[8,11,14,38,53,56,105,126,135],"mean":[9],"or":[10,23],"median":[12],"of":[13,31,34,37,55,142,164,174,198],"predictive":[15,39,162],"probability":[16,40],"distribution,":[17],"via":[18],"training":[19],"with":[20,125,171],"a":[21,49,122,140,194],"squared":[22],"an":[24],"absolute":[25,196],"error":[26],"scoring":[27,129],"function.":[28],"The":[29,116],"importance":[30],"issuing":[32],"predictions":[33],"more":[35,101],"functionals":[36,103],"distribution":[41],"(quantiles":[42],"and":[43,68,74,89,111,154,184],"expectiles)":[44],"has":[45],"been":[46],"recognized":[47],"as":[48,91,113],"means":[50],"quantify":[52],"uncertainty":[54],"prediction.":[57],"In":[58,159],"deep":[59,80],"(DL)":[61],"applications,":[62],"that":[63,86,107],"is":[64,119,132],"possible":[65],"through":[66],"quantile":[67,82,128,137],"expectile":[69],"neural":[71],"networks":[72,84],"(QRNN":[73],"ERNN":[75,90],"respectively).":[76],"Here":[77],"we":[78],"introduce":[79],"Huber":[81,97,127,136],"(DHQRN)":[85],"nest":[87,109],"QRNN":[88],"edge":[92],"cases.":[93,115],"DHQRN":[94,144],"can":[95],"predict":[96,148],"quantiles,":[98],"which":[99,131],"are":[100,145,168],"general":[102],"in":[104,151],"sense":[106],"they":[108],"quantiles":[110],"expectiles":[112],"limiting":[114],"main":[117],"idea":[118],"train":[121],"DL":[123,166],"algorithm":[124],"function,":[130],"consistent":[133],"for":[134],"functional.":[138],"As":[139],"proof":[141],"concept,":[143],"applied":[146],"house":[149],"prices":[150],"Melbourne,":[152],"Australia":[153],"Boston,":[155],"United":[156],"States":[157],"(US).":[158],"this":[160],"context,":[161],"performances":[163],"three":[165],"architectures":[167],"discussed":[169],"along":[170],"evidential":[172],"interpretation":[173],"results":[175],"from":[176],"two":[177],"economic":[178],"case":[179,188],"studies.":[180],"Additional":[181],"simulation":[182],"experiments":[183],"real-world":[187],"studies":[189],"using":[190],"open":[191],"datasets":[192],"demonstrate":[193],"satisfactory":[195],"performance":[197],"DHQRN.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2}],"updated_date":"2026-01-19T04:01:09.351973","created_date":"2025-10-10T00:00:00"}