{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T08:53:52Z","timestamp":1730278432605,"version":"3.28.0"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,9,20]],"date-time":"2020-09-20T00:00:00Z","timestamp":1600560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,9,20]],"date-time":"2020-09-20T00:00:00Z","timestamp":1600560000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,20]],"date-time":"2020-09-20T00:00:00Z","timestamp":1600560000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,20]]},"DOI":"10.1109\/itsc45102.2020.9294272","type":"proceedings-article","created":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T23:14:55Z","timestamp":1608851695000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["MultiMix: A Multi-Task Deep Learning Approach for Travel Mode Identification with Few GPS Data"],"prefix":"10.1109","author":[{"given":"Xiaozhuang","family":"Song","sequence":"first","affiliation":[]},{"given":"Christos","family":"Markos","sequence":"additional","affiliation":[]},{"given":"James J.Q.","family":"Yu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2019.8730799"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"caruana","year":"1997","journal-title":"Machine Learning"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/BF00332918"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31750-2_5"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967249"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3267529"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.11.021"},{"key":"ref14","first-page":"1163","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","author":"sajjadi","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref16","first-page":"3365","article-title":"Learning with pseudoensembles","author":"bachman","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref17","first-page":"2","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"lee","year":"2013","journal-title":"Workshop on Challenges in Representation Learning 30th International Conference on Machine Learning"},{"key":"ref18","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semisupervised deep learning results","author":"tarvainen","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref19","article-title":"There are many consistent explanations of unlabeled data: Why you should average","author":"athiwaratkun","year":"2019","journal-title":"International Conference on Learning Representations"},{"key":"ref28","first-page":"11190","article-title":"Unlabeled data improves adversarial robustness","author":"carmon","year":"2019","journal-title":"Neural Information Processing System Vancouver"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/1409635.1409677"},{"key":"ref27","first-page":"107299","article-title":"Selective review of offline change point detection methods","author":"truong","year":"2019","journal-title":"Signal Processing"},{"key":"ref3","article-title":"Transport-domain applications of widely used data sources in the smart transportation: A survey","author":"dabiri","year":"2018","journal-title":"arXiv preprint arXiv 1803 10902"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbspro.2014.07.239"},{"key":"ref29","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"journal-title":"Using GPS Data Loggers To Replace Travel Diaries In the Collection of Travel Data","year":"2000","author":"wolf","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsams.2008.10.010"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.trpro.2015.12.020"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2011.2158001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi6020057"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/info7040067"},{"key":"ref20","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2018","journal-title":"International Conference on Learning Representations (ICLR) Vancouver"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2896985"},{"key":"ref21","first-page":"5050","article-title":"Mixmatch: A holistic approach to semi-supervised learning","author":"berthelot","year":"2019","journal-title":"Neural Information Processing System Vancouver"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1179\/sre.1975.23.176.88"},{"key":"ref23","first-page":"32","article-title":"Geolife: A collaborative social networking service among user, location and trajectory","volume":"33","author":"zheng","year":"2010","journal-title":"IEEE Data(base) Engineering Bulletin"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367532"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1080\/01441647.2014.903530"}],"event":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","start":{"date-parts":[[2020,9,20]]},"location":"Rhodes, Greece","end":{"date-parts":[[2020,9,23]]}},"container-title":["2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9294153\/9294168\/09294272.pdf?arnumber=9294272","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:57:54Z","timestamp":1656453474000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9294272\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,20]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/itsc45102.2020.9294272","relation":{},"subject":[],"published":{"date-parts":[[2020,9,20]]}}}