{"id":"https://openalex.org/W4399324214","doi":"https://doi.org/10.1145/3643832.3661435","title":"Poster: Fast On-Device Adaptation with Approximate Forward Training","display_name":"Poster: Fast On-Device Adaptation with Approximate Forward Training","publication_year":2024,"publication_date":"2024-06-03","ids":{"openalex":"https://openalex.org/W4399324214","doi":"https://doi.org/10.1145/3643832.3661435"},"language":"en","primary_location":{"id":"doi:10.1145/3643832.3661435","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3643832.3661435","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661435","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661435","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027008963","display_name":"M.S. Shim","orcid":"https://orcid.org/0009-0003-8786-1152"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Minkyu Shim","raw_affiliation_strings":["Department of Computer Science, Seoul National University, Seoul, Korea, South"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Seoul National University, Seoul, Korea, South","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114626700","display_name":"Youngki Lee","orcid":"https://orcid.org/0000-0002-1319-7071"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngki Lee","raw_affiliation_strings":["Department of Computer Science, Seoul National University, Seoul, Korea, South"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Seoul National University, Seoul, Korea, South","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027008963"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05315733,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"712","last_page":"713"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9814000129699707,"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"}},"topics":[{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9814000129699707,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9742000102996826,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9607999920845032,"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/computer-science","display_name":"Computer science","score":0.7394295930862427},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.7363356351852417},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.719788134098053},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06076860427856445},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.04449531435966492}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394295930862427},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.7363356351852417},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.719788134098053},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06076860427856445},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.04449531435966492},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3643832.3661435","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3643832.3661435","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661435","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3643832.3661435","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3643832.3661435","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3643832.3661435","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399324214.pdf"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W2734941459","https://openalex.org/W3102587717","https://openalex.org/W4200223557","https://openalex.org/W4312968147"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W230091440","https://openalex.org/W2390279801","https://openalex.org/W2233261550","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Enabling":[0],"real-time":[1],"machine":[2],"learning":[3],"(ML)":[4],"model":[5],"adaptation":[6],"to":[7,81,88],"previously":[8],"unseen,":[9],"but":[10],"highly":[11],"specific":[12,93],"contexts":[13],"and":[14,23],"environments":[15],"can":[16],"vastly":[17],"extend":[18],"the":[19,34,53,60,83,86,89,103,109,116],"capability":[20],"of":[21,32,40,62,91,106,118],"mobile":[22,49,122],"ubiquitous":[24],"AI":[25],"systems.":[26],"Cloud-aided":[27],"approaches":[28,44],"often":[29],"fall":[30],"short":[31],"meeting":[33],"time":[35],"constraints":[36],"without":[37,64],"assuming":[38],"pre-acquisition":[39],"data.":[41],"Other":[42],"existing":[43],"targeting":[45],"efficient":[46],"training":[47,67,79,100],"on":[48,52,121],"devices":[50],"focus":[51],"generally":[54],"complex":[55],"context-agnostic":[56],"tasks":[57],"where":[58],"achieving":[59],"performance":[61],"DNNs":[63],"proper":[65],"backpropagation-based":[66],"is":[68],"unlikely.":[69],"In":[70],"this":[71,119],"work,":[72],"we":[73],"introduce":[74],"a":[75,92],"novel":[76],"approximate":[77],"forward":[78],"scheme":[80],"leverage":[82],"relationship":[84],"that":[85],"updates":[87],"parameters":[90],"linear":[94,104],"(and":[95],"convolutional)":[96],"layer":[97],"in":[98],"each":[99],"step":[101],"are":[102],"combinations":[105],"outputs":[107],"from":[108],"previous":[110],"layer.":[111],"Our":[112],"preliminary":[113],"results":[114],"demonstrate":[115],"feasibility":[117],"approach":[120],"platforms.":[123]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}