{"id":"https://openalex.org/W3153345798","doi":"https://doi.org/10.1145/3442381.3450051","title":"Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning","display_name":"Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning","publication_year":2021,"publication_date":"2021-04-19","ids":{"openalex":"https://openalex.org/W3153345798","doi":"https://doi.org/10.1145/3442381.3450051","mag":"3153345798"},"language":"en","primary_location":{"id":"doi:10.1145/3442381.3450051","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3450051","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3442381.3450051","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101664149","display_name":"Letian Zhang","orcid":"https://orcid.org/0000-0002-6920-2052"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Letian Zhang","raw_affiliation_strings":["University of Miami, USA"],"affiliations":[{"raw_affiliation_string":"University of Miami, USA","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085364987","display_name":"Lixing Chen","orcid":"https://orcid.org/0000-0002-1805-0183"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lixing Chen","raw_affiliation_strings":["University of Miami, USA"],"affiliations":[{"raw_affiliation_string":"University of Miami, USA","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044771462","display_name":"Jie Xu","orcid":"https://orcid.org/0000-0002-0515-1647"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Xu","raw_affiliation_strings":["University of Miami, USA"],"affiliations":[{"raw_affiliation_string":"University of Miami, USA","institution_ids":["https://openalex.org/I145608581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101664149"],"corresponding_institution_ids":["https://openalex.org/I145608581"],"apc_list":null,"apc_paid":null,"fwci":7.6813,"has_fulltext":false,"cited_by_count":69,"citation_normalized_percentile":{"value":0.9754394,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3111","last_page":"3123"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.989300012588501,"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/T13052","display_name":"Molecular Communication and Nanonetworks","score":0.9887999892234802,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8115571737289429},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5980471968650818},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.589016318321228},{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.5538703203201294},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.49895620346069336},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4933759272098541},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.48664984107017517},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.46616512537002563},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4540565311908722},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.4439224600791931},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4380737543106079},{"id":"https://openalex.org/keywords/mobile-edge-computing","display_name":"Mobile edge computing","score":0.4196832776069641},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.29119813442230225},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.18033969402313232},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.17979973554611206},{"id":"https://openalex.org/keywords/operating-system","display_name":"Operating system","score":0.15253809094429016}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8115571737289429},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5980471968650818},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.589016318321228},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.5538703203201294},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.49895620346069336},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4933759272098541},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.48664984107017517},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.46616512537002563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4540565311908722},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.4439224600791931},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4380737543106079},{"id":"https://openalex.org/C2776061582","wikidata":"https://www.wikidata.org/wiki/Q25325231","display_name":"Mobile edge computing","level":3,"score":0.4196832776069641},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.29119813442230225},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.18033969402313232},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.17979973554611206},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.15253809094429016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3442381.3450051","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3450051","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3442381.3450051","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3442381.3450051","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Web Conference 2021","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2133665775","https://openalex.org/W2194775991","https://openalex.org/W2400861403","https://openalex.org/W2583383421","https://openalex.org/W2585425668","https://openalex.org/W2615459164","https://openalex.org/W2624989916","https://openalex.org/W2775394714","https://openalex.org/W2798170643","https://openalex.org/W2809251854","https://openalex.org/W2883929540","https://openalex.org/W2887817291","https://openalex.org/W2931743911","https://openalex.org/W2962755172","https://openalex.org/W2963037989","https://openalex.org/W2965289829","https://openalex.org/W2971714613","https://openalex.org/W2980856918","https://openalex.org/W3104263540","https://openalex.org/W4302590882"],"related_works":["https://openalex.org/W4289229149","https://openalex.org/W4221146321","https://openalex.org/W2963827161","https://openalex.org/W2805376489","https://openalex.org/W2987375765","https://openalex.org/W3004668712","https://openalex.org/W4302363080","https://openalex.org/W2917288403","https://openalex.org/W3047552222","https://openalex.org/W4224133481"],"abstract_inverted_index":{"Recent":[0],"breakthroughs":[1],"in":[2,19,234],"deep":[3,36,71],"learning":[4,139,183,193],"(DL)":[5],"have":[6],"led":[7],"to":[8,68,99,105,126,145,157,215],"the":[9,20,52,81,90,94,101,107,129,148,160,163,217,242],"emergence":[10],"of":[11,54,64,162,176,219,236],"many":[12],"intelligent":[13],"mobile":[14,29,42,82],"applications":[15],"and":[16,44,58,84,221,240],"services,":[17],"but":[18,196],"meanwhile":[21],"also":[22,197],"pose":[23],"unprecedented":[24],"computing":[25],"challenges":[26],"on":[27,80,89,114,120,208],"resource-constrained":[28,41],"devices.":[30],"This":[31],"paper":[32],"builds":[33],"a":[34,40,45,70,76,85,121,136,179,209],"collaborative":[35],"inference":[37,109,244],"system":[38,66,134,164,207,238],"between":[39],"device":[43,83],"powerful":[46],"edge":[47,91],"server,":[48,92],"aiming":[49],"at":[50],"joining":[51],"power":[53],"both":[55],"on-device":[56],"processing":[57],"computation":[59],"offloading.":[60],"The":[61,174,225],"basic":[62],"idea":[63],"this":[65],"is":[67,155,178,198],"partition":[69,103,131,150],"neural":[72],"network":[73],"(DNN)":[74],"into":[75],"front-end":[77],"part":[78,87],"running":[79,88],"back-end":[86],"with":[93],"key":[95],"challenge":[96],"being":[97],"how":[98],"locate":[100],"optimal":[102,130,149],"point":[104,151],"minimize":[106],"end-to-end":[108,243],"delay.":[110,245],"Unlike":[111],"existing":[112],"efforts":[113],"DNN":[115],"partitioning":[116],"that":[117,228],"rely":[118],"heavily":[119],"dedicated":[122],"offline":[123],"profiling":[124],"stage":[125],"search":[127],"for":[128,170,200],"point,":[132],"our":[133,206],"has":[135,190],"built-in":[137],"online":[138],"module,":[140],"called":[141,185],"Autodidactic":[142],"Neurosurgeon":[143],"(ANS),":[144],"automatically":[146],"learn":[147],"on-the-fly.":[152],"Therefore,":[153],"ANS":[154,177,220,229],"able":[156],"closely":[158],"follow":[159],"changes":[161,239],"environment":[165],"by":[166],"generating":[167],"new":[168],"knowledge":[169],"adaptive":[171],"decision":[172],"making.":[173],"core":[175],"novel":[180],"contextual":[181],"bandit":[182],"algorithm,":[184],"\u03bcLinUCB,":[186],"which":[187],"not":[188],"only":[189],"provable":[191],"theoretical":[192],"performance":[194],"guarantee":[195],"ultra-lightweight":[199],"easy":[201],"real-world":[202],"implementation.":[203],"We":[204],"implement":[205],"video":[210],"stream":[211],"object":[212],"detection":[213],"testbed":[214],"validate":[216],"design":[218],"evaluate":[222],"its":[223],"performance.":[224],"experiments":[226],"show":[227],"significantly":[230],"outperforms":[231],"state-of-the-art":[232],"benchmarks":[233],"terms":[235],"tracking":[237],"reducing":[241]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}