{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T03:45:32Z","timestamp":1772595932114,"version":"3.50.1"},"reference-count":44,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1109\/ijcnn.2018.8489153","type":"proceedings-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T22:25:09Z","timestamp":1539987909000},"page":"1-6","source":"Crossref","is-referenced-by-count":13,"title":["Partial Adversarial Training for Prediction Interval"],"prefix":"10.1109","author":[{"given":"H M Dipu","family":"Kabir","sequence":"first","affiliation":[]},{"given":"Abbas","family":"Khosravi","sequence":"additional","affiliation":[]},{"given":"M Anwar","family":"Hosen","sequence":"additional","affiliation":[]},{"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.mejo.2017.10.002"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/BMAS.2004.1393990"},{"key":"ref33","first-page":"6405","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","author":"lakshminarayanan","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref32","first-page":"326","article-title":"Lda based paper currency recognition system using edge histogram descriptor","author":"rahman","year":"2014","journal-title":"Computer and Information Technology (ICCIT) 2014 17th International Conference on IEEE"},{"key":"ref31","article-title":"The space of transferable adversarial examples","author":"tram\u00e8r","year":"2017","journal-title":"arXiv preprint arXiv 1704 03453"},{"key":"ref30","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"0","journal-title":"Advances in neural information processing systems"},{"key":"ref37","first-page":"1","article-title":"Constructing optimal prediction intervals by using neural networks and bootstrap method","author":"khosravi","year":"2014","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref36","article-title":"No need to worry about adversarial examples in object detection in autonomous vehicles","author":"lu","year":"2017","journal-title":"arXiv preprint arXiv 1707 07816"},{"key":"ref35","article-title":"Regularizing deep networks using efficient layerwise adversarial training","author":"sankaranarayanan","year":"2017","journal-title":"arXiv preprint arXiv 1705 07819"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.07.016"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00009"},{"key":"ref40","article-title":"Recent advances in electricity price forecasting: A review of probabilistic forecasting","author":"nowotarski","year":"2017","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-017-1684-6"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-016-1532-0"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LED.2017.2679201"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engfracmech.2016.05.009"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engfracmech.2017.05.013"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2014.977446"},{"key":"ref17","first-page":"5580","article-title":"What uncertainties do we need in bayesian deep learning for computer vision?","author":"kendall","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref18","first-page":"1431","article-title":"Chained gaussian processes","author":"saul","year":"2016","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref19","article-title":"Uncertainty decomposition in bayesian neural networks with latent variables","author":"depeweg","year":"2017","journal-title":"arXiv preprint arXiv 1706 08495"},{"key":"ref28","article-title":"Standard detectors aren’t (currently) fooled by physical adversarial stop signs","author":"lu","year":"2017","journal-title":"arXiv preprint arXiv 1710 03337"},{"key":"ref4","article-title":"Ensemble adversarial training: Attacks and defenses","author":"tram\u00e8r","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2017.11.019"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SIEDS.2017.7937699"},{"key":"ref6","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"arXiv preprint arXiv 1412 6572"},{"key":"ref29","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"ganin","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2593488"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICITIS.2010.5689567"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081950"},{"key":"ref9","article-title":"Voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks","author":"hsu","year":"2017","journal-title":"arXiv preprint arXiv 1704 00849"},{"key":"ref1","first-page":"98","article-title":"A bayesian approach to filtering junk e-mail","volume":"62","author":"sahami","year":"1998","journal-title":"Learning for Text Categorization Papers from the 1998 Workshop"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2014.03.060"},{"key":"ref22","first-page":"1478","article-title":"Mondrian forests for large-scale regression when uncertainty matters","author":"lakshminarayanan","year":"2016","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2013.6622314"},{"key":"ref42","year":"2017","journal-title":"Uk national grid status"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CCECE.2017.7946723"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2016.7737705"},{"key":"ref23","article-title":"Concrete problems in ai safety","author":"amodei","year":"2016","journal-title":"arXiv preprint arXiv 1606 06565"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2276053"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/PESGM.2016.7741880"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/EMS.2010.34"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2016.2612645"}],"event":{"name":"2018 International Joint Conference on Neural Networks (IJCNN)","location":"Rio de Janeiro","start":{"date-parts":[[2018,7,8]]},"end":{"date-parts":[[2018,7,13]]}},"container-title":["2018 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8465565\/8488986\/08489153.pdf?arnumber=8489153","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:38:43Z","timestamp":1598229523000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8489153\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2018.8489153","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}