{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:16:13Z","timestamp":1771949773824,"version":"3.50.1"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"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":[[2019,7]]},"DOI":"10.1109\/ijcnn.2019.8852105","type":"proceedings-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T03:44:32Z","timestamp":1569901472000},"page":"1-8","source":"Crossref","is-referenced-by-count":56,"title":["ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification"],"prefix":"10.1109","author":[{"given":"Kathan","family":"Kashiparekh","sequence":"first","affiliation":[]},{"given":"Jyoti","family":"Narwariya","sequence":"additional","affiliation":[]},{"given":"Pankaj","family":"Malhotra","sequence":"additional","affiliation":[]},{"given":"Lovekesh","family":"Vig","sequence":"additional","affiliation":[]},{"given":"Gautam","family":"Shroff","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref32","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"European Conference on Computer Vision"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.226"},{"key":"ref30","article-title":"Timenet: Pre-trained deep recurrent neural network for time series classification","author":"malhotra","year":"2017"},{"key":"ref10","article-title":"Towards a universal neural network encoder for time series","author":"serr\u00e0","year":"2018"},{"key":"ref11","article-title":"Transfer learning for time series classification","author":"fawaz","year":"2018"},{"key":"ref12","article-title":"Multi-scale convolutional neural networks for time series classification","author":"cui","year":"2016"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref14","article-title":"Chrononet: A deep recurrent neural network for abnormal eeg identification","author":"roy","year":"2018"},{"key":"ref15","article-title":"The ucr time series classification archive","author":"chen","year":"2015"},{"key":"ref16","article-title":"Timenet: Pre-trained deep recurrent neural network for time series classification","author":"malhotra","year":"2017","journal-title":"Proceedings of the European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning (ESANN)"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0483-9"},{"key":"ref18","article-title":"Data augmentation for time series classification using convolutional neural networks","author":"le guennec","year":"2016","journal-title":"ECML\/PKDD Workshop on Adv Anal and Learn on Temporal Data"},{"key":"ref19","article-title":"Data augmentation using synthetic data for time series classification with deep residual networks","author":"fawaz","year":"2018"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2416723"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref27","first-page":"3630","article-title":"Matching networks for one shot learning","author":"vinyals","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref3","article-title":"Deep learning for time series classification: a review","author":"fawaz","year":"2018"},{"key":"ref6","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0377-7"},{"key":"ref5","first-page":"17","article-title":"Deep learning of representations for unsupervised and transfer learning","author":"bengio","year":"2012","journal-title":"Proc ICML Workshop Unsupervised Transfer Learn"},{"key":"ref8","article-title":"Using features from pre-trained timenet for clinical predictions","author":"gupta","year":"2018","journal-title":"The 3rd International Workshop on Knowledge Discovery in Healthcare Data at IJCAI"},{"key":"ref7","first-page":"607","article-title":"TimeNet: Pre-trained deep recurrent neural network for time series classification","author":"malhotra","year":"2017","journal-title":"25th European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2779939"},{"key":"ref9","article-title":"Transfer learning for clinical time series analysis using recurrent neural networks","author":"gupta","year":"2018","journal-title":"ACM SIGKDD workshop on Machine Learning for Medicine and Healthcare"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref20","article-title":"Regularizing fully convolutional networks for time series classification by decorrelating filters","author":"kaushal","year":"2019","journal-title":"Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)"},{"key":"ref22","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref24","article-title":"Network in network","author":"lin","year":"2013"},{"key":"ref23","article-title":"Fusing features based on signal properties and timenet for time series classification","author":"ukil","year":"2019","journal-title":"Proceedings of the European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning (ESANN)"},{"key":"ref26","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"event":{"name":"2019 International Joint Conference on Neural Networks (IJCNN)","location":"Budapest, Hungary","start":{"date-parts":[[2019,7,14]]},"end":{"date-parts":[[2019,7,19]]}},"container-title":["2019 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8840768\/8851681\/08852105.pdf?arnumber=8852105","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:48:14Z","timestamp":1658094494000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8852105\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2019.8852105","relation":{},"subject":[],"published":{"date-parts":[[2019,7]]}}}