{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T10:55:27Z","timestamp":1779188127388,"version":"3.51.4"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Ford-Gerogia Tech Alliance Project through the Georgia Tech Ford Alliance"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tai.2023.3246959","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T13:49:30Z","timestamp":1677246570000},"page":"38-50","source":"Crossref","is-referenced-by-count":13,"title":["Gaussian Switch Sampling: A Second-Order Approach to Active Learning"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9634-9359","authenticated-orcid":false,"given":"Ryan","family":"Benkert","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8743-7058","authenticated-orcid":false,"given":"Mohit","family":"Prabhushankar","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6818-8001","authenticated-orcid":false,"given":"Ghassan","family":"AlRegib","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armin","family":"Pacharmi","sequence":"additional","affiliation":[{"name":"Ford Motor Company, Dearborn, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique","family":"Corona","sequence":"additional","affiliation":[{"name":"Ford Motor Company, Dearborn, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2022.3163871"},{"key":"ref4","article-title":"On the difficulty of warm-starting neural network training","author":"Ash","year":"2019"},{"key":"ref5","article-title":"Deep batch active learning by diverse, uncertain gradient lower bounds","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ash","year":"2020"},{"issue":"Mar","key":"ref6","first-page":"255","article-title":"Online choice of active learning algorithms","volume":"5","author":"Baram","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00976"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506644"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3178112"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897514"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2006.886952"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.2200\/s00832ed1v01y201802aim037"},{"key":"ref13","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","volume-title":"Proc. 14th Int. Conf. Artif. Intell. Statist.","author":"Coates","year":"2011"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1613\/jair.295"},{"key":"ref15","article-title":"Selection via proxy: Efficient data selection for deep learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Coleman","year":"2020"},{"key":"ref16","article-title":"CINIC-10 is not ImageNet or CIFAR-10","author":"Darlow","year":"2018"},{"key":"ref17","article-title":"On statistical bias in active learning: How and when to fix it","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Farquhar","year":"2021"},{"key":"ref18","article-title":"Bayesian convolutional neural networks with Bernoulli approximate variational inference","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gal","year":"2016"},{"key":"ref19","first-page":"1183","article-title":"Deep Bayesian active learning with image data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gal","year":"2017"},{"key":"ref20","article-title":"Deep active learning over the long tail","author":"Geifman","year":"2017"},{"key":"ref21","article-title":"Discriminative active learning","author":"Gissin","year":"2019"},{"key":"ref22","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1214\/19-aos1867"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IV47402.2020.9304793"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","first-page":"1","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hendrycks","year":"2019"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143897"},{"key":"ref28","article-title":"Bayesian active learning for classification and preference learning","author":"Houlsby","year":"2011"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9597"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref33","article-title":"Batchbald: Efficient and diverse batch acquisition for deep Bayesian active learning","volume":"32","author":"Kirsch","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref34","first-page":"18685","article-title":"Similar: Submodular information measures based active learning in realistic scenarios","volume":"34","author":"Kothawade","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803228"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58589-1_13"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190706"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190679"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506430"},{"key":"ref40","article-title":"Contrastive reasoning in neural networks","author":"Prabhushankar","year":"2021"},{"key":"ref41","article-title":"Introspective learning: A two-stage approach for inference in neural networks","author":"Prabhushankar","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190927"},{"key":"ref43","first-page":"3738","article-title":"Online structured laplace approximations for overcoming catastrophic forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ritter","year":"2018"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/11871842_40"},{"key":"ref45","first-page":"839","article-title":"Less is more: Active learning with support vector machines","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Schohn","year":"2000"},{"key":"ref46","article-title":"Active learning for convolutional neural networks: A core-set approach","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Sener","year":"2018"},{"key":"ref47","article-title":"Active learning literature survey","author":"Settles","year":"2009"},{"key":"ref48","first-page":"6747","article-title":"Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima","volume":"34","author":"Shi","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref49","first-page":"1","article-title":"CURE-TSR: Challenging unreal and real environments for traffic sign recognition","volume-title":"Proc. Neural Inf. Process. Syst. Workshop Mach. Learn. Intell. Transp. Syst.","author":"Temel","year":"2017"},{"key":"ref50","first-page":"3650","article-title":"Optimal subsampling with influence functions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ting","year":"2018"},{"key":"ref51","article-title":"An empirical study of example forgetting during deep neural network learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Toneva","year":"2019"},{"key":"ref52","article-title":"Active learning: Theory and applications","author":"Tong","year":"2001"},{"issue":"Nov","key":"ref53","first-page":"45","article-title":"Support vector machine active learning with applications to text classification","volume":"2","author":"Tong","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref54","first-page":"9690","article-title":"Uncertainty estimation using a single deep deterministic neural network","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Amersfoort","year":"2020"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2014.6889457"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1292914"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/10384607\/10053381.pdf?arnumber=10053381","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:08:41Z","timestamp":1755911321000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10053381\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":56,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tai.2023.3246959","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}