{"id":"https://openalex.org/W7225002131","doi":"https://doi.org/10.5281/zenodo.23835584","title":"Self-Evolving Neural ODEs with Dwell-Time-Constrained Structural Compression","display_name":"Self-Evolving Neural ODEs with Dwell-Time-Constrained Structural Compression","publication_year":2026,"publication_date":"2026-10-31","ids":{"openalex":"https://openalex.org/W7225002131","doi":"https://doi.org/10.5281/zenodo.23835584"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.23835584","doi":"https://doi.org/10.5281/zenodo.23835584","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835584","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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.23835584","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101629955","display_name":"Gurpreet Singh","orcid":"https://orcid.org/0000-0002-6484-2704","observed_orcids":["https://orcid.org/0000-0002-6484-2704"]},"institutions":[{"id":"https://openalex.org/I79161377","display_name":"Punjabi University","ror":"https://ror.org/00xdn8y92","country_code":"IN","type":"education","lineage":["https://openalex.org/I79161377"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Singh, Gurpreet","raw_affiliation_strings":["Punjabi University, Patiala"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Punjabi University, Patiala","institution_ids":["https://openalex.org/I79161377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5160246475","display_name":"Dr.Jasvir Singh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I79161377","display_name":"Punjabi University","ror":"https://ror.org/00xdn8y92","country_code":"IN","type":"education","lineage":["https://openalex.org/I79161377"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Singh, Dr.Jasvir","raw_affiliation_strings":["Punjabi University, Patiala"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Punjabi University, Patiala","institution_ids":["https://openalex.org/I79161377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I79161377"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.7759000062942505,"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/T10320","display_name":"Neural Networks and Applications","score":0.7759000062942505,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.09430000185966492,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0284000001847744,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/neural-odes","display_name":"neural ordinary differential equations","score":0.9580000042915344},{"id":"https://openalex.org/keywords/dwell-time-constraints","display_name":"dwell time constraints","score":0.7059999704360962},{"id":"https://openalex.org/keywords/binary-masking","display_name":"binary masking","score":0.6129999756813049},{"id":"https://openalex.org/keywords/tucker-decomposition","display_name":"Tucker decomposition","score":0.7889999747276306},{"id":"https://openalex.org/keywords/fmri-classification","display_name":"fMRI classification","score":0.6150000095367432},{"id":"https://openalex.org/keywords/continuous-time-neural-networks","display_name":"continuous-time neural networks","score":0.7129999995231628},{"id":"https://openalex.org/keywords/structural-adaptation","display_name":"structural adaptation","score":0.31299999356269836}],"concepts":[{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.6970999836921692},{"id":"https://openalex.org/C200331156","wikidata":"https://www.wikidata.org/wiki/Q506041","display_name":"Jacobian matrix and determinant","level":2,"score":0.640500009059906},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5870000123977661},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5236999988555908},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47620001435279846},{"id":"https://openalex.org/C22324862","wikidata":"https://www.wikidata.org/wiki/Q652707","display_name":"Lipschitz continuity","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.447299987077713},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40720000863075256},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C51544822","wikidata":"https://www.wikidata.org/wiki/Q465274","display_name":"Ordinary differential equation","level":3,"score":0.3617999851703644},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3578000068664551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35370001196861267},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.31189998984336853},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2644999921321869},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.26249998807907104}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.23835584","doi":"https://doi.org/10.5281/zenodo.23835584","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835584","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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.23835585","doi":"https://doi.org/10.5281/zenodo.23835585","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835585","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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.23835584","doi":"https://doi.org/10.5281/zenodo.23835584","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23835584","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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3177092097","https://openalex.org/W4409122893","https://openalex.org/W4304192845","https://openalex.org/W4415796198","https://openalex.org/W3021268132","https://openalex.org/W7123563605","https://openalex.org/W3167040424","https://openalex.org/W7019688694","https://openalex.org/W7101615203","https://openalex.org/W4391650031"],"abstract_inverted_index":{"We":[0],"propose":[1],"an":[2,69,161,199,228,299],"adaptive":[3,303],"latent-width":[4],"framework":[5,87,284],"for":[6,247,275],"continuous-time":[7,307,314],"neural":[8,308],"networks":[9],"that":[10,60],"dynamically":[11],"regulates":[12],"the":[13,52,62,127,158,170,190,222,240,253,261,265,269,283],"number":[14],"of":[15,51,64,135,140,164,181,201,239,271],"active":[16,151,186],"latent":[17,53,65,142,152,311],"coordinates":[18,143],"during":[19],"training.":[20],"The":[21,86,217],"proposed":[22],"architecture":[23],"integrates":[24],"a":[25,30,40,57,76,97,132,147,178,194,206,211,231,244,321],"factor-weighted":[26],"encoder":[27],"derived":[28],"from":[29],"Tucker":[31],"decomposition":[32],"with":[33,68,137,205],"Liquid":[34],"Time-Constant":[35],"(LTC)":[36],"dynamics":[37],"formulated":[38],"as":[39],"Neural":[41],"Ordinary":[42],"Differential":[43],"Equation":[44],"(Neural":[45],"ODE).":[46],"To":[47],"facilitate":[48],"structured":[49],"adaptation":[50,319],"representation,":[54],"we":[55],"introduce":[56],"spectral-energy":[58,116],"probe":[59],"monitors":[61],"contribution":[63],"components,":[66],"together":[67],"importance-based":[70],"binary":[71],"masking":[72],"mechanism":[73],"governed":[74],"by":[75],"minimum":[77],"dwell-time":[78],"constraint":[79],"to":[80,146,302],"prevent":[81],"excessively":[82],"frequent":[83],"structural":[84,237,304],"updates.":[85],"is":[88,107],"evaluated":[89],"on":[90,264],"three":[91],"distinct":[92],"benchmarks:":[93],"MNIST,":[94,126],"CIFAR-100,":[95,169],"and":[96,120,177,243,268,316],"single-subject":[98,191],"functional":[99],"Magnetic":[100],"Resonance":[101],"Imaging":[102],"(fMRI)":[103],"dataset.":[104],"Its":[105],"performance":[106,212,263],"assessed":[108],"through":[109],"predictive":[110],"accuracy,":[111],"active-state":[112],"compression,":[113],"representational":[114],"similarity,":[115],"diagnostics,":[117],"sensitivity":[118],"bounds,":[119],"checkpoint-level":[121],"Jacobian":[122,259],"spectral":[123],"analysis.":[124],"On":[125,168,189],"final":[128],"experimental":[129],"run":[130],"achieves":[131,160],"test":[133,175],"accuracy":[134,163,176,200],"97.51%,":[136],"only":[138],"104":[139],"256":[141],"active,":[144],"corresponding":[145],"59.38%":[148],"reduction":[149],"in":[150,221,257,306],"dimensionality.":[153],"Across":[154],"five":[155],"random":[156],"seeds,":[157],"model":[159],"average":[162],"97.47":[165],"\u00b1":[166,203],"0.17%.":[167],"selected":[171],"checkpoint":[172,258],"attains":[173],"48.02%":[174],"macro-F1":[179],"score":[180],"0.4766,":[182],"while":[183],"retaining":[184],"109":[185],"ODE":[187],"coordinates.":[188],"fMRI":[192,266],"benchmark,":[193,267],"six-fold":[195],"leave-one-run-out":[196],"evaluation":[197],"yields":[198],"43.63":[202],"6.86%,":[204],"separate":[207],"temporal-interval":[208],"analysis":[209],"identifying":[210],"peak":[213],"at":[214],"4":[215],"seconds.":[216],"theoretical":[218],"analysis,":[219],"provided":[220],"appendix,":[223],"establishes":[224],"boundedness,":[225],"Lipschitz":[226],"sensitivity,":[227],"energy-balance":[229],"identity,":[230],"perturbation":[232],"bound":[233],"under":[234],"latent-coordinate":[235],"collapse,":[236],"properties":[238],"width-adaptation":[241],"schedule,":[242],"sufficient":[245],"condition":[246],"local":[248],"contraction.":[249],"Despite":[250],"these":[251],"results,":[252],"observed":[254],"positive":[255],"eigenvalues":[256],"spectra,":[260],"moderate":[262],"absence":[270],"direct":[272],"runtime":[273],"measurements":[274],"masked":[276],"implementations":[277],"highlight":[278],"important":[279],"limitations.":[280],"In":[281],"particular,":[282],"does":[285],"not":[286],"establish":[287],"global":[288],"stability":[289],"or":[290],"demonstrate":[291],"practical":[292],"inference-time":[293],"acceleration.":[294],"Overall,":[295],"this":[296],"work":[297],"presents":[298],"integrated":[300],"approach":[301],"evolution":[305],"networks,":[309],"combining":[310],"representation":[312],"control,":[313],"dynamics,":[315],"importance-driven":[317],"dimensional":[318],"within":[320],"unified":[322],"learning":[323],"framework.":[324]},"counts_by_year":[],"updated_date":"2026-11-02T09:45:03.842371","created_date":"2026-11-01T00:00:00"}