{"id":"https://openalex.org/W4416249437","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227859","title":"Residual Reservoir Memory Networks","display_name":"Residual Reservoir Memory Networks","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249437","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227859"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.09925","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000660715","display_name":"Matteo Pinna","orcid":"https://orcid.org/0000-0002-4784-7255"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Matteo Pinna","raw_affiliation_strings":["University of Pisa,Department of Computer Science,Pisa,Italy"],"affiliations":[{"raw_affiliation_string":"University of Pisa,Department of Computer Science,Pisa,Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046284911","display_name":"Andrea Ceni","orcid":"https://orcid.org/0000-0002-5084-0505"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Andrea Ceni","raw_affiliation_strings":["University of Pisa,Department of Computer Science,Pisa,Italy"],"affiliations":[{"raw_affiliation_string":"University of Pisa,Department of Computer Science,Pisa,Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011604061","display_name":"Claudio Gallicchio","orcid":"https://orcid.org/0000-0002-6692-2564"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Claudio Gallicchio","raw_affiliation_strings":["University of Pisa,Department of Computer Science,Pisa,Italy"],"affiliations":[{"raw_affiliation_string":"University of Pisa,Department of Computer Science,Pisa,Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5000660715"],"corresponding_institution_ids":["https://openalex.org/I108290504"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17903436,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9911999702453613,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9911999702453613,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.002400000113993883,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reservoir-computing","display_name":"Reservoir computing","score":0.8816999793052673},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.84170001745224},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5867999792098999},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5809000134468079},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4729999899864197},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4472000002861023},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4390999972820282},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.3395000100135803}],"concepts":[{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.8816999793052673},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.84170001745224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6039000153541565},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4729999899864197},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4472000002861023},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4390999972820282},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40230000019073486},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3395000100135803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3142000138759613},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C118702147","wikidata":"https://www.wikidata.org/wiki/Q189396","display_name":"Dynamic random-access memory","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.29739999771118164},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C14641988","wikidata":"https://www.wikidata.org/wiki/Q7315329","display_name":"Reservoir modeling","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C2778668878","wikidata":"https://www.wikidata.org/wiki/Q6380338","display_name":"Reservoir simulation","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2508.09925","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09925","pdf_url":"https://arxiv.org/pdf/2508.09925","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"pmh:doi:10.48550/arxiv.2508.09925","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:zenodo.org:18459603","is_oa":true,"landing_page_url":"https://doi.org/10.1109/IJCNN64981.2025.11227859","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IJCNN 2025, International Joint Conference on Neural Networks 2025","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.09925","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09925","pdf_url":"https://arxiv.org/pdf/2508.09925","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2001263627","https://openalex.org/W2057755457","https://openalex.org/W2118706537","https://openalex.org/W2159682675","https://openalex.org/W2171865010","https://openalex.org/W2608997467","https://openalex.org/W2887258823","https://openalex.org/W2988244882","https://openalex.org/W3204457379","https://openalex.org/W3214007446","https://openalex.org/W4392135912","https://openalex.org/W4392498941","https://openalex.org/W4399146357","https://openalex.org/W4399207005","https://openalex.org/W4402363872"],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,25,30],"novel":[3],"class":[4],"of":[5,50,63,96],"untrained":[6],"Recurrent":[7],"Neural":[8],"Networks":[9,21],"(RNNs)":[10],"within":[11],"the":[12,34,43,51,61,73,94,97],"Reservoir":[13,19],"Computing":[14],"(RC)":[15],"paradigm,":[16],"called":[17],"Residual":[18],"Memory":[20],"(ResRMNs).":[22],"ResRMN":[23],"combines":[24],"linear":[26,64],"memory":[27],"reservoir":[28,55],"with":[29],"non-linear":[31],"reservoir,":[32],"where":[33],"latter":[35],"is":[36,80,106],"based":[37],"on":[38,83],"residual":[39,75],"orthogonal":[40],"connections":[41],"along":[42],"temporal":[44,74],"dimension":[45],"for":[46,72],"enhanced":[47],"long-term":[48],"propagation":[49],"input.":[52],"The":[53,77],"resulting":[54],"state":[56],"dynamics":[57],"are":[58],"studied":[59],"through":[60],"lens":[62],"stability":[65],"analysis,":[66],"and":[67,85],"we":[68],"investigate":[69],"diverse":[70],"configurations":[71],"connections.":[76],"proposed":[78,98],"approach":[79,99],"empirically":[81],"assessed":[82],"time-series":[84],"pixel-level":[86],"1-D":[87],"classification":[88],"tasks.":[89],"Our":[90],"experimental":[91],"results":[92],"highlight":[93],"advantages":[95],"over":[100],"other":[101],"conventional":[102],"RC":[103],"models.":[104],"Code":[105],"available":[107],"at":[108],"github.com/NennoMP/residualrmn":[109]},"counts_by_year":[],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
