{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T20:48:05Z","timestamp":1775940485249,"version":"3.50.1"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,6]]},"DOI":"10.1109\/icassp39728.2021.9413883","type":"proceedings-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T19:53:45Z","timestamp":1620935625000},"page":"3545-3549","source":"Crossref","is-referenced-by-count":44,"title":["Semi-Supervised Time Series Classification by Temporal Relation Prediction"],"prefix":"10.1109","author":[{"given":"Haoyi","family":"Fan","sequence":"first","affiliation":[]},{"given":"Fengbin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ruidong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xunhua","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zuoyong","family":"Li","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.12.026"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47426-3_39"},{"key":"ref12","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"},{"key":"ref13","article-title":"Temporal ensembling for semi-supervised learning","author":"laine","year":"2017","journal-title":"5th International Conference on Learning Representations"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref15","article-title":"Un-supervised representation learning by predicting image rotations","author":"gidaris","year":"2018","journal-title":"6th International Conference on Learning Representations ICLR"},{"key":"ref16","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"2020","journal-title":"Proc of the International Conference on Machine Learning (ICML)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00840"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58520-4_30"},{"key":"ref19","article-title":"Representation learning with contrastive predictive coding","author":"van den oord","year":"2018"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"6645","DOI":"10.1109\/ICASSP.2013.6638947","article-title":"Speech recognition with deep recurrent neural networks","author":"graves","year":"2013","journal-title":"IEEE International Conference on Acoustics Speech and Signal Processing"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-811968-6.00009-7"},{"key":"ref6","article-title":"Somvae: Interpretable discrete representation learning on time series","author":"fortuin","year":"2019","journal-title":"ICLR International Conference on Learning Representations"},{"key":"ref5","article-title":"Interpolation-prediction networks for irregularly sampled time series","author":"shukla","year":"2019","journal-title":"ICLR International Conference on Learning Representations"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487633"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150498"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15432-4"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.02.030"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2460242"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2019-1873"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053569"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2019-2605"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1016\/j.isatra.2020.08.010","article-title":"Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study","author":"zhao","year":"2020"},{"key":"ref26","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"van der maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1145\/3136755.3136817","article-title":"Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks","author":"um","year":"2017","journal-title":"Proceedings of the 19th ACM International Conference on Multimodal Interaction"}],"event":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Toronto, ON, Canada","start":{"date-parts":[[2021,6,6]]},"end":{"date-parts":[[2021,6,11]]}},"container-title":["ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9413349\/9413350\/09413883.pdf?arnumber=9413883","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T08:29:04Z","timestamp":1672129744000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9413883\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,6]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/icassp39728.2021.9413883","relation":{},"subject":[],"published":{"date-parts":[[2021,6,6]]}}}