{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:36Z","timestamp":1750220316382,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T00:00:00Z","timestamp":1641600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,1,8]]},"DOI":"10.1145\/3493700.3493711","type":"proceedings-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T23:54:21Z","timestamp":1641599661000},"page":"46-53","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Time Series Representation Learning with Contrastive Triplet Selection"],"prefix":"10.1145","author":[{"given":"Yuan-Chi","family":"Chang","sequence":"first","affiliation":[{"name":"IBM Research AI, US"}]},{"given":"Dharmashankar","family":"Subramanian","sequence":"additional","affiliation":[{"name":"IBM Research AI, US"}]},{"given":"Raju","family":"Pavuluri","sequence":"additional","affiliation":[{"name":"IBM Research AI, US"}]},{"given":"Timothy","family":"Dinger","sequence":"additional","affiliation":[{"name":"IBM Research AI, US"}]}],"member":"320","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68474-1_3"},{"key":"e_1_3_2_1_2_1","volume-title":"Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh.","author":"Bagnall Anthony","year":"2018","unstructured":"Anthony Bagnall, Hoang\u00a0Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 2018. The UEA multivariate time series classification archive, 2018. arXiv preprint arXiv:1811.00075(2018)."},{"key":"e_1_3_2_1_3_1","unstructured":"Shaojie Bai J\u00a0Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271(2018)."},{"key":"e_1_3_2_1_4_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709(2020)."},{"key":"e_1_3_2_1_5_1","unstructured":"Hoang\u00a0Anh Dau Eamonn Keogh Kaveh Kamgar Chin-Chia\u00a0Michael Yeh Yan Zhu Shaghayegh Gharghabi Chotirat\u00a0Ann Ratanamahatana Yanping Bing Hu Nurjahan Begum Anthony Bagnall Abdullah Mueen Gustavo Batista and Hexagon-ML. 2018. The UCR Time Series Classification Archive."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171\u20134186."},{"key":"e_1_3_2_1_7_1","unstructured":"Jean-Yves Franceschi Aymeric Dieuleveut and Martin Jaggi. 2019. Unsupervised scalable representation learning for multivariate time series. In Advances in Neural Information Processing Systems. 4652\u20134663. https:\/\/github.com\/White-Link\/UnsupervisedScalableRepresentationLearningTimeSeries."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.307"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097)","author":"Huang Jiabo","year":"2019","unstructured":"Jiabo Huang, Qi Dong, Shaogang Gong, and Xiatian Zhu. 2019. Unsupervised Deep Learning by Neighbourhood Discovery. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, California, USA, 2849\u20132858. http:\/\/proceedings.mlr.press\/v97\/huang19b.html"},{"key":"e_1_3_2_1_10_1","unstructured":"Longlong Jing and Yingli Tian. 2019. Self-supervised visual feature learning with deep neural networks: A survey. arXiv preprint arXiv:1902.06162(2019)."},{"key":"e_1_3_2_1_11_1","unstructured":"Jeff Johnson Matthijs Douze and Herv\u00e9 J\u00e9gou. 2017. Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734(2017)."},{"key":"e_1_3_2_1_12_1","volume-title":"On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and knowledge discovery 7, 4","author":"Keogh Eamonn","year":"2003","unstructured":"Eamonn Keogh and Shruti Kasetty. 2003. On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining and knowledge discovery 7, 4 (2003), 349\u2013371."},{"key":"e_1_3_2_1_13_1","volume-title":"Similarity preserving representation learning for time series clustering. IJCAI\u201919","author":"Lei Qi","year":"2019","unstructured":"Qi Lei, Jinfeng Yi, Roman Vaculin, Lingfei Wu, and Inderjit\u00a0S Dhillon. 2019. Similarity preserving representation learning for time series clustering. IJCAI\u201919 (2019), 2845\u20132851."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3182382"},{"key":"e_1_3_2_1_15_1","unstructured":"Pankaj Malhotra Vishnu TV Lovekesh Vig Puneet Agarwal and Gautam Shroff. 2017. TimeNet: Pre-trained deep recurrent neural network for time series classification. arXiv preprint arXiv:1706.08838(2017)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3371158.3371162"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372278.3390732"},{"key":"e_1_3_2_1_19_1","unstructured":"Aaron van\u00a0den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner Andrew Senior and Koray Kavukcuoglu. 2016. Wavenet: a generative model for raw audio. arXiv preprint arXiv:1609.03499v2(2016)."},{"volume-title":"2017 International Joint Conference on Neural Networks (IJCNN). 1578\u20131585","author":"Wang Z.","key":"e_1_3_2_1_20_1","unstructured":"Z. Wang, W. Yan, and T. Oates. 2017. Time series classification from scratch with deep neural networks: A strong baseline. In 2017 International Joint Conference on Neural Networks (IJCNN). 1578\u20131585."},{"key":"e_1_3_2_1_21_1","volume-title":"Jinfeng Yi, Fangli Xu, Qi Lei, and Michael Witbrock.","author":"Wu Lingfei","year":"2018","unstructured":"Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi, Fangli Xu, Qi Lei, and Michael Witbrock. 2018. Random Warping Series: A Random Features Method for Time-Series Embedding(Proceedings of Machine Learning Research, Vol.\u00a084), Amos Storkey and Fernando Perez-Cruz (Eds.). PMLR, Playa Blanca, Lanzarote, Canary Islands, 793\u2013802. http:\/\/proceedings.mlr.press\/v84\/wu18b.html"},{"volume-title":"Improved Embeddings with Easy Positive Triplet Mining. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). 2463\u20132471","author":"Xuan H.","key":"e_1_3_2_1_22_1","unstructured":"H. Xuan, A. Stylianou, and R. Pless. 2020. Improved Embeddings with Easy Positive Triplet Mining. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). 2463\u20132471."},{"key":"e_1_3_2_1_23_1","unstructured":"Zhihan Yue Yujing Wang Juanyong Duan Tianmeng Yang Congrui Huang and Bixiong Xu. 2021. Learning Timestamp-Level Representations for Time Series with Hierarchical Contrastive Loss. CoRR abs\/2106.10466(2021). arxiv:2106.10466https:\/\/arxiv.org\/abs\/2106.10466"},{"key":"e_1_3_2_1_24_1","volume-title":"SONG: Approximate Nearest Neighbor Search on GPU. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1033\u20131044","author":"Zhao Weijie","year":"2020","unstructured":"Weijie Zhao, Shulong Tan, and Ping Li. 2020. SONG: Approximate Nearest Neighbor Search on GPU. In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 1033\u20131044."}],"event":{"name":"CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)","sponsor":["SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"],"location":"Bangalore India","acronym":"CODS-COMAD 2022"},"container-title":["Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3493700.3493711","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3493700.3493711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:51Z","timestamp":1750191111000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3493700.3493711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,8]]},"references-count":24,"alternative-id":["10.1145\/3493700.3493711","10.1145\/3493700"],"URL":"https:\/\/doi.org\/10.1145\/3493700.3493711","relation":{},"subject":[],"published":{"date-parts":[[2022,1,8]]},"assertion":[{"value":"2022-01-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}