{"id":"https://openalex.org/W7140872569","doi":"https://doi.org/10.1109/ieeeconf67917.2025.11443886","title":"Learning Efficient Implicit Neural Representations for Low Data Settings with Structured Noise","display_name":"Learning Efficient Implicit Neural Representations for Low Data Settings with Structured Noise","publication_year":2025,"publication_date":"2025-10-26","ids":{"openalex":"https://openalex.org/W7140872569","doi":"https://doi.org/10.1109/ieeeconf67917.2025.11443886"},"language":null,"primary_location":{"id":"doi:10.1109/ieeeconf67917.2025.11443886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf67917.2025.11443886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 59th Asilomar Conference on Signals, Systems, and Computers","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/A5084629908","display_name":"Kushal Vyas","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kushal Vyas","raw_affiliation_strings":["Rice University,Electrical and Computer Engineering,Houston,USA"],"affiliations":[{"raw_affiliation_string":"Rice University,Electrical and Computer Engineering,Houston,USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073971393","display_name":"Ashok Veeraraghavan","orcid":"https://orcid.org/0000-0001-5043-7460"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ashok Veeraraghavan","raw_affiliation_strings":["Rice University,Electrical and Computer Engineering,Houston,USA"],"affiliations":[{"raw_affiliation_string":"Rice University,Electrical and Computer Engineering,Houston,USA","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5130437034","display_name":"Guha Balakrishnan","orcid":null},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guha Balakrishnan","raw_affiliation_strings":["Rice University,Electrical and Computer Engineering,Houston,USA"],"affiliations":[{"raw_affiliation_string":"Rice University,Electrical and Computer Engineering,Houston,USA","institution_ids":["https://openalex.org/I74775410"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084629908"],"corresponding_institution_ids":["https://openalex.org/I74775410"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87012848,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"594","last_page":"598"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.20919999480247498,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.20919999480247498,"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/T10320","display_name":"Neural Networks and Applications","score":0.20200000703334808,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0674000009894371,"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/noise","display_name":"Noise (video)","score":0.5494999885559082},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42890000343322754},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38339999318122864},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.3483000099658966},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3434000015258789},{"id":"https://openalex.org/keywords/background-noise","display_name":"Background noise","score":0.2863999903202057}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291999816894531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5810999870300293},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5494999885559082},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42890000343322754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3483000099658966},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31529998779296875},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.29420000314712524},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29179999232292175},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25760000944137573},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ieeeconf67917.2025.11443886","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ieeeconf67917.2025.11443886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 59th Asilomar Conference on Signals, Systems, and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338440","display_name":"HORIZON EUROPE Health","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2057612871","https://openalex.org/W2133665775","https://openalex.org/W2150534249","https://openalex.org/W2151035455","https://openalex.org/W3109585842","https://openalex.org/W3177583232","https://openalex.org/W3178317539","https://openalex.org/W3198632145","https://openalex.org/W4221151978","https://openalex.org/W4296057677","https://openalex.org/W4296807546","https://openalex.org/W4312233582","https://openalex.org/W4312978380","https://openalex.org/W4319300205","https://openalex.org/W4386066651","https://openalex.org/W4386072459","https://openalex.org/W4386076276","https://openalex.org/W4402703005","https://openalex.org/W4404645945","https://openalex.org/W4406861140","https://openalex.org/W7103749801"],"related_works":[],"abstract_inverted_index":{"Implicit":[0],"neural":[1],"representations":[2],"(INRs)":[3],"use":[4],"multi-layer":[5],"perceptron":[6],"models":[7],"to":[8,17,29,41,95,165],"learn":[9,30],"a":[10,24,62,116,131,139,158],"function":[11],"mapping":[12],"the":[13,101],"signal\u2019s":[14],"input":[15],"coordinates":[16],"its":[18,42,48],"values.":[19],"INRs":[20,93,136,151],"have":[21,56],"emerged":[22],"as":[23,115],"novel":[25],"and":[26,33,51,64,74,84,137,167],"promising":[27,159],"method":[28],"high-quality,":[31],"compact,":[32],"continuous":[34],"signal":[35,143],"representations,":[36],"but":[37],"are":[38,79],"notoriously":[39],"sensitive":[40],"underlying":[43],"parameter":[44],"initialization,":[45],"strongly":[46],"affecting":[47],"reconstruction":[49],"quality":[50],"convergence.":[52],"Recent":[53],"data-driven":[54],"methods":[55],"addressed":[57],"these":[58],"shortcomings":[59],"by":[60],"learning":[61],"robust":[63,148],"generalizable":[65,109],"initialization":[66,118,133],"for":[67,119,135,142,150,161],"INRs.":[68],"However,":[69],"they":[70],"rely":[71],"on":[72,154],"large":[73],"domain-specific":[75,155],"training":[76],"datasets,":[77],"which":[78],"rarely":[80],"available":[81],"in":[82],"scientific":[83,166],"medical":[85,168],"imaging.":[86,169],"In":[87],"our":[88],"study,":[89],"we":[90,129],"find":[91],"that":[92,99,111],"fitted":[94],"random":[96],"noise":[97],"images":[98,106,122],"mimic":[100],"spectral":[102],"structure":[103],"of":[104],"natural":[105],"yield":[107],"highly":[108],"features":[110],"can":[112],"later":[113],"serve":[114],"powerful":[117],"fitting":[120],"unseen":[121],"at":[123],"test":[124],"time.":[125],"Leveraging":[126],"this":[127],"observation,":[128],"propose":[130],"data-free":[132],"strategy":[134],"present":[138],"preliminary":[140],"analysis":[141],"fitting.":[144],"Our":[145],"approach":[146],"enables":[147],"initializations":[149],"without":[152],"relying":[153],"data,":[156],"offering":[157],"direction":[160],"low-data":[162],"scenarios":[163],"common":[164]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-03-27T00:00:00"}
