{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:14:14Z","timestamp":1740132854068,"version":"3.37.3"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01DC009834"],"award-info":[{"award-number":["R01DC009834"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["SC1821301"],"award-info":[{"award-number":["SC1821301"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF","award":["CNS-1544895","IIS-1715858","IIS-1717654","IIS-1844885","IIS-1915083"],"award-info":[{"award-number":["CNS-1544895","IIS-1715858","IIS-1717654","IIS-1844885","IIS-1915083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Signal Process. Lett."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/lsp.2021.3072292","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T19:30:29Z","timestamp":1617996629000},"page":"867-871","source":"Crossref","is-referenced-by-count":2,"title":["Geometric Analysis of Uncertainty Sampling for Dense Neural Network Layer"],"prefix":"10.1109","volume":"28","author":[{"given":"Aziz","family":"Kocanaogullari","sequence":"first","affiliation":[]},{"given":"Niklas","family":"Smedemark-Margulies","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5094-1931","authenticated-orcid":false,"given":"Murat","family":"Akcakaya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1114-3539","authenticated-orcid":false,"given":"Deniz","family":"Erdogmus","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"article-title":"The Caltech-UCSD birds-200-2011 dataset","year":"2011","author":"wah","key":"ref32"},{"article-title":"Fine-grained visual classification of aircraft","year":"2013","author":"maji","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1126\/science.aab3050"},{"key":"ref37","first-page":"740","article-title":"Microsoft coco: Common objects in context","author":"lin","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6706807"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"article-title":"J. Kim, and N. Fox-Gieg, “The Quick, Draw!-AI experiment","year":"0","author":"jongejan","key":"ref34"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/e18020051"},{"key":"ref11","first-page":"1885","article-title":"Understanding black-box predictions via influence functions","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","first-page":"9291","article-title":"Representer point selection for explaining deep neural networks","author":"yeh","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44816-0_31"},{"key":"ref16","article-title":"Active learning for convolutional neural networks: A core-set approach","author":"sener","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2589879"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3115\/1613715.1613855"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0781-x"},{"key":"ref28","article-title":"Meta-dataset: A dataset of datasets for learning to learn from few examples","author":"triantafillou","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","first-page":"7957","article-title":"Fast and flexible multi-task classification using conditional neural adaptive processes","author":"requeima","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"article-title":"Active learning literature survey","year":"2009","author":"settles","key":"ref6"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref8","first-page":"1870","article-title":"Active nearest neighbors in changing environments","author":"berlind","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"ref2","first-page":"721","article-title":"Tadam: Task dependent adaptive metric for improved few-shot learning","author":"oreshkin","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683089"},{"key":"ref1","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"0","journal-title":"Proc the 34th Int Conf Mach Learn"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5019-5"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1982.tb01195.x"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/TPAMI.2006.156","article-title":"Confidence-based active learning","volume":"28","author":"li","year":"2006","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/71.1.1"},{"journal-title":"Pattern Recognition and Machine Learning","year":"2006","author":"bishop","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1613\/jair.295"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007568008032"}],"container-title":["IEEE Signal Processing Letters"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/97\/9325893\/9399790-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/97\/9325893\/09399790.pdf?arnumber=9399790","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T19:57:45Z","timestamp":1649188665000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9399790\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/lsp.2021.3072292","relation":{},"ISSN":["1070-9908","1558-2361"],"issn-type":[{"type":"print","value":"1070-9908"},{"type":"electronic","value":"1558-2361"}],"subject":[],"published":{"date-parts":[[2021]]}}}