{"id":"https://openalex.org/W2963175113","doi":"https://doi.org/10.24963/ijcai.2018/302","title":"Energy-efficient Amortized Inference with Cascaded Deep Classifiers","display_name":"Energy-efficient Amortized Inference with Cascaded Deep Classifiers","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2963175113","doi":"https://doi.org/10.24963/ijcai.2018/302","mag":"2963175113"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2018/302","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/302","pdf_url":"https://www.ijcai.org/proceedings/2018/0302.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2018/0302.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102329823","display_name":"Jiaqi Guan","orcid":"https://orcid.org/0000-0002-4515-0198"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Jiaqi Guan","raw_affiliation_strings":["Tsinghua University","University of Illinois at Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100355861","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-3660-6464"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057648803","display_name":"Qiang Liu","orcid":"https://orcid.org/0000-0003-1037-2185"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Liu","raw_affiliation_strings":["University of Texas at Austin"],"affiliations":[{"raw_affiliation_string":"University of Texas at Austin","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061177999","display_name":"Jian Peng","orcid":"https://orcid.org/0000-0002-1736-2978"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jian Peng","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061177999"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.29944285,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.81523903,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2184","last_page":"2190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987000226974487,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9968000054359436,"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/computer-science","display_name":"Computer science","score":0.8388279676437378},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6870255470275879},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6339606046676636},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6325982809066772},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6316595077514648},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.619137167930603},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5727971792221069},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5508383512496948},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.5481763482093811},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.522674024105072},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4887263774871826},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4188457131385803},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4149588346481323},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.326591432094574},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1712070107460022}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8388279676437378},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6870255470275879},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6339606046676636},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6325982809066772},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6316595077514648},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.619137167930603},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5727971792221069},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5508383512496948},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.5481763482093811},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.522674024105072},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4887263774871826},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4188457131385803},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4149588346481323},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.326591432094574},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1712070107460022},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2018/302","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/302","pdf_url":"https://www.ijcai.org/proceedings/2018/0302.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2018/302","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2018/302","pdf_url":"https://www.ijcai.org/proceedings/2018/0302.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8999999761581421}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963175113.pdf","grobid_xml":"https://content.openalex.org/works/W2963175113.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1934410531","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2119144962","https://openalex.org/W2119717200","https://openalex.org/W2163605009","https://openalex.org/W2168894214","https://openalex.org/W2179423374","https://openalex.org/W2194775991","https://openalex.org/W2274287116","https://openalex.org/W2279098554","https://openalex.org/W2302255633","https://openalex.org/W2325237720","https://openalex.org/W2474389331","https://openalex.org/W2562731582","https://openalex.org/W2739542029","https://openalex.org/W2741430497","https://openalex.org/W2747261267","https://openalex.org/W2963125010","https://openalex.org/W2963518064","https://openalex.org/W2964137095","https://openalex.org/W2964350391","https://openalex.org/W3118608800","https://openalex.org/W4295116917","https://openalex.org/W4297775537","https://openalex.org/W4297813615","https://openalex.org/W6791858558","https://openalex.org/W6803771590","https://openalex.org/W6834709193"],"related_works":["https://openalex.org/W2153719181","https://openalex.org/W1971748923","https://openalex.org/W1566155057","https://openalex.org/W2060986072","https://openalex.org/W2052574922","https://openalex.org/W64588465","https://openalex.org/W3120641340","https://openalex.org/W2117825986","https://openalex.org/W3042419602","https://openalex.org/W2966649771"],"abstract_inverted_index":{"Deep":[0],"neural":[1,65,94],"networks":[2,66,95],"have":[3],"been":[4],"remarkable":[5],"successful":[6],"in":[7,103],"various":[8],"AI":[9],"tasks":[10],"but":[11],"often":[12],"cast":[13],"high":[14],"computation":[15,148],"and":[16,40,70,96,119,132,149,158,178],"energy":[17,41,150],"cost":[18,42,118],"for":[19,87],"energy-constrained":[20],"applications":[21],"such":[22],"as":[23],"mobile":[24],"sensing.":[25],"We":[26],"address":[27],"this":[28,88],"problem":[29],"by":[30,107,134],"proposing":[31],"a":[32,61,71,80,113],"novel":[33],"framework":[34],"that":[35],"optimizes":[36],"the":[37,97,108,116,120,130],"prediction":[38],"accuracy":[39,131],"simultaneously,":[43],"thus":[44],"enabling":[45],"effective":[46],"cost-accuracy":[47],"trade-off":[48,114],"at":[49],"test":[50],"time.":[51],"In":[52],"our":[53,169],"framework,":[54],"each":[55],"data":[56,89],"instance":[57],"is":[58,74,125],"pushed":[59],"into":[60],"cascade":[62,92],"of":[63,93],"deep":[64],"with":[67,172],"increasing":[68],"sizes,":[69],"selection":[72,98],"module":[73,99],"used":[75,86],"to":[76,111,127,136,140,146,156,162],"sequentially":[77],"determine":[78],"when":[79],"sufficiently":[81,143],"accurate":[82,144],"classifier":[83],"can":[84],"be":[85],"instance.":[90],"The":[91],"are":[100],"jointly":[101],"trained":[102],"an":[104],"end-to-end":[105],"fashion":[106],"REINFORCE":[109],"algorithm":[110],"optimize":[112],"between":[115],"computational":[117],"predictive":[121],"accuracy.":[122,165],"Our":[123],"method":[124],"able":[126],"simultaneously":[128],"improve":[129],"efficiency":[133],"learning":[135],"assign":[137],"easy":[138],"instances":[139,155],"fast":[141],"yet":[142],"classifiers":[145,161],"save":[147],"cost,":[151],"while":[152],"assigning":[153],"harder":[154],"deeper":[157],"more":[159],"powerful":[160],"ensure":[163],"satisfiable":[164],"Moreover,":[166],"we":[167],"demonstrate":[168],"method's":[170],"effectiveness":[171],"extensive":[173],"experiments":[174],"on":[175],"CIFAR-10/100,":[176],"ImageNet32x32":[177],"original":[179],"ImageNet":[180],"dataset.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}