{"id":"https://openalex.org/W7158681810","doi":"https://doi.org/10.48550/arxiv.2604.26489","title":"Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective","display_name":"Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7158681810","doi":"https://doi.org/10.48550/arxiv.2604.26489"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.26489","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26489","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,"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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.26489","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134881292","display_name":"Jiancheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiancheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102866693","display_name":"Mingjia Yin","orcid":"https://orcid.org/0009-0005-0853-1089"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Mingjia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134876404","display_name":"Hao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134896741","display_name":"Enhong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Enhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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/T10203","display_name":"Recommender Systems and Techniques","score":0.8151999711990356,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10203","display_name":"Recommender Systems and Techniques","score":0.8151999711990356,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.029899999499320984,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.023600000888109207,"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/feature","display_name":"Feature (linguistics)","score":0.7214000225067139},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.6955000162124634},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6539999842643738},{"id":"https://openalex.org/keywords/feature-matching","display_name":"Feature matching","score":0.5382999777793884},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.3449999988079071},{"id":"https://openalex.org/keywords/empirical-evidence","display_name":"Empirical evidence","score":0.29649999737739563}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7214000225067139},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.6955000162124634},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6539999842643738},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5819000005722046},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.5382999777793884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.504800021648407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35749998688697815},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","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":0.28999999165534973},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2702000141143799},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.26489","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26489","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.26489","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26489","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,"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":"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"DNNs":[0,27,45,90,125],"have":[1,40],"gained":[2],"widespread":[3],"adoption":[4],"in":[5,46],"feature":[6,35,105],"interaction":[7,106],"recommendation":[8],"models.":[9],"However,":[10],"there":[11],"has":[12],"been":[13],"a":[14,63,109,135],"longstanding":[15],"debate":[16],"on":[17,74,103],"their":[18,72],"roles.":[19],"On":[20],"one":[21],"hand,":[22],"some":[23],"works":[24],"claim":[25],"that":[26,120],"possess":[28],"the":[29,42,68,75,79,129,144],"ability":[30],"to":[31,66],"implicitly":[32],"capture":[33],"high-order":[34],"interactions.":[36,57],"Conversely,":[37],"recent":[38],"studies":[39],"highlighted":[41],"limitations":[43],"of":[44,70,78,100,113,132,147],"effectively":[47,127],"learning":[48],"dot":[49],"products,":[50],"specifically":[51],"second-order":[52],"interactions,":[53],"let":[54],"alone":[55],"higher-order":[56],"In":[58,81],"this":[59],"paper,":[60],"we":[61,83],"present":[62],"novel":[64],"perspective":[65],"understand":[67],"effectiveness":[69],"DNNs:":[71],"impact":[73],"dimensional":[76,130,148],"robustness":[77],"representations.":[80],"particular,":[82],"conduct":[84],"extensive":[85],"experiments":[86],"involving":[87],"both":[88,121],"parallel":[89,122],"and":[91,123],"stacked":[92,124],"DNNs.":[93,116],"Our":[94],"evaluation":[95],"encompasses":[96],"an":[97],"overall":[98],"study":[99],"complete":[101],"DNN":[102],"two":[104],"models,":[107],"alongside":[108],"fine-grained":[110],"ablation":[111],"analysis":[112],"components":[114],"within":[115],"Experimental":[117],"results":[118],"demonstrate":[119],"can":[126],"mitigate":[128],"collapse":[131],"embeddings.":[133],"Furthermore,":[134],"gradient-based":[136],"theoretical":[137],"analysis,":[138],"supported":[139],"by":[140],"empirical":[141],"evidence,":[142],"uncovers":[143],"underlying":[145],"mechanisms":[146],"collapse.":[149]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-01T00:00:00"}
