{"id":"https://openalex.org/W7117472911","doi":"https://doi.org/10.1109/dicta68720.2025.11302450","title":"Flow-Selectivity SSM: A Generative State-Space Model with History-Aware GFlowNet Policies","display_name":"Flow-Selectivity SSM: A Generative State-Space Model with History-Aware GFlowNet Policies","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7117472911","doi":"https://doi.org/10.1109/dicta68720.2025.11302450"},"language":null,"primary_location":{"id":"doi:10.1109/dicta68720.2025.11302450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta68720.2025.11302450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","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/A5020530172","display_name":"Nadhir Hassen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121368","display_name":"Machine Science","ror":"https://ror.org/02hrr9v50","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210121368"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nadhir Hassen","raw_affiliation_strings":["Australian Institute of Machine Learning"],"affiliations":[{"raw_affiliation_string":"Australian Institute of Machine Learning","institution_ids":["https://openalex.org/I4210121368"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035480544","display_name":"Johan Verjans","orcid":"https://orcid.org/0000-0002-8336-6774"},"institutions":[{"id":"https://openalex.org/I4210121368","display_name":"Machine Science","ror":"https://ror.org/02hrr9v50","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210121368"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Johan Verjans","raw_affiliation_strings":["Australian Institute of Machine Learning"],"affiliations":[{"raw_affiliation_string":"Australian Institute of Machine Learning","institution_ids":["https://openalex.org/I4210121368"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020530172"],"corresponding_institution_ids":["https://openalex.org/I4210121368"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.53067067,"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":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.30559998750686646,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.30559998750686646,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0843999981880188,"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"}},{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.06419999897480011,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6687999963760376},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6000000238418579},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5472999811172485},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5029000043869019},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.49970000982284546},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4828999936580658},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4641000032424927},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.4607999920845032}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7142000198364258},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6687999963760376},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6000000238418579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5511000156402588},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5029000043869019},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5001999735832214},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49970000982284546},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4641000032424927},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.4607999920845032},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.45159998536109924},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3815000057220459},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3441999852657318},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.33869999647140503},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.322299987077713},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C184408114","wikidata":"https://www.wikidata.org/wiki/Q1502022","display_name":"Generative Design","level":3,"score":0.28049999475479126},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.2671000063419342},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta68720.2025.11302450","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta68720.2025.11302450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.647331953048706,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2151669316","https://openalex.org/W3127238141","https://openalex.org/W3177318507","https://openalex.org/W4382203079","https://openalex.org/W4385245566"],"related_works":[],"abstract_inverted_index":{"Structured":[0],"State-Space":[1,148],"Models":[2],"(SSMs)":[3],"represent":[4],"a":[5,27,35,49,63,79,85,97,105,137,155,178],"new":[6],"frontier":[7],"in":[8,100,185],"sequence":[9,61],"modeling,":[10],"yet":[11],"their":[12],"evolution":[13],"from":[14],"Linear":[15],"Time-Invariant":[16],"(LTI)":[17],"systems":[18],"to":[19,47,90,153,161],"content-aware":[20],"models":[21],"like":[22,198],"Mamba":[23],"has":[24],"relied":[25],"on":[26,200],"local,":[28],"input-dependent":[29],"selection":[30,57],"mechanism.":[31],"This":[32,55,175],"paper":[33],"introduces":[34],"more":[36],"powerful":[37],"paradigm:":[38],"Flow-Selectivity,":[39],"which":[40],"uses":[41],"the":[42,112,130],"SSM's":[43],"entire":[44],"compressed":[45],"history":[46],"parameterize":[48],"Generative":[50,147],"Flow":[51],"Network":[52],"(GFlowNet)":[53],"policy.":[54,114],"history-aware":[56],"enables":[58],"goal-directed,":[59],"structured":[60,187],"generation,":[62],"capability":[64],"beyond":[65],"standard":[66],"autoregressive":[67],"models.":[68],"Our":[69],"technical":[70],"contributions":[71],"are":[72],"threefold:":[73],"1)":[74],"We":[75,95,116],"formulate":[76],"synthesis":[77],"as":[78,136],"Markov":[80],"Decision":[81],"Process":[82],"(MDP)":[83],"over":[84],"space":[86,157],"of":[87,158],"chemical":[88],"reactions":[89],"ensure":[91],"synthetic":[92],"accessibility.":[93],"2)":[94],"solve":[96],"key":[98],"challenge":[99],"such":[101],"MDPs":[102],"by":[103],"introducing":[104],"robust,":[106],"entropy-regularized":[107],"REINFORCE":[108],"objective":[109],"for":[110,142,182],"training":[111],"backward":[113],"3)":[115],"enhance":[117],"exploration":[118],"with":[119,168],"novel":[120],"FFT-based":[121],"learning":[122],"signals.":[123],"Experimentally,":[124],"we":[125],"demonstrate":[126],"state-of-theart":[127],"performance.":[128],"For":[129],"sEH":[131],"inhibitor":[132],"design":[133],"task,":[134],"framed":[135],"3D":[138],"structural":[139],"fitting":[140],"problem":[141],"protein":[143],"pocket":[144],"imaging,":[145],"our":[146,191],"Model":[149],"(GSSM)":[150],"is":[151],"able":[152],"filter":[154],"vast":[156],"building":[159],"blocks":[160],"generate":[162],"novel,":[163],"diverse,":[164],"and":[165],"synthesizable":[166],"molecules":[167],"high":[169],"binding":[170],"affinity,":[171],"outperforming":[172],"strong":[173],"baselines.":[174],"work":[176],"presents":[177],"powerful,":[179],"general":[180],"framework":[181],"steerable":[183],"generation":[184],"complex,":[186],"domains.":[188],"In":[189],"addition,":[190],"GSSM":[192],"significantly":[193],"outperforms":[194],"modern":[195],"time-series":[196],"benchmarks":[197],"PatchTST":[199],"non-stationary":[201],"EEG":[202],"forecasting.":[203]},"counts_by_year":[],"updated_date":"2025-12-30T23:08:21.542490","created_date":"2025-12-29T00:00:00"}