{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T04:06:59Z","timestamp":1783829219286,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS-1750074, CNS-1816497"],"award-info":[{"award-number":["IIS-1750074, CNS-1816497"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539140","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"3270-3278","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Towards Learning Disentangled Representations for Time Series"],"prefix":"10.1145","author":[{"given":"Yuening","family":"Li","sequence":"first","affiliation":[{"name":"Texas A&amp;M University &amp; NEC Labs America, College Station, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengzhang","family":"Chen","sequence":"additional","affiliation":[{"name":"NEC Labs America, Princeton, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daochen","family":"Zha","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengnan","family":"Du","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchao","family":"Ni","sequence":"additional","affiliation":[{"name":"NEC Labs America, Princeton, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"NEC Labs America, Princeton, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Chen","sequence":"additional","affiliation":[{"name":"NEC Labs America, Princeton, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Hu","sequence":"additional","affiliation":[{"name":"Rice University, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446","author":"Ajakan Hana","year":"2014","unstructured":"Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, and Mario Marchand. 2014. Domain-adversarial neural networks. arXiv preprint arXiv:1412.4446 (2014)."},{"key":"e_1_3_2_2_2_1","unstructured":"Davide Anguita and et al. 2013. A public domain dataset for human activity recognition using smartphones. In Esann."},{"key":"e_1_3_2_2_3_1","unstructured":"Shaojie Bai et al. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271 (2018)."},{"key":"e_1_3_2_2_4_1","volume-title":"A theory of learning from different domains. Machine learning","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning (2010)."},{"key":"e_1_3_2_2_5_1","volume-title":"Domain adaptation--can quantity compensate for quality? Annals of Mathematics and Artificial Intelligence","author":"Ben-David Shai","year":"2014","unstructured":"Shai Ben-David and Ruth Urner. 2014. Domain adaptation--can quantity compensate for quality? Annals of Mathematics and Artificial Intelligence (2014)."},{"key":"e_1_3_2_2_6_1","unstructured":"Christopher P Burgess Irina Higgins Arka Pal Loic Matthey Nick Watters et al. 2018. Understanding disentangling in beta-VAE. arXiv:1804.03599 (2018)."},{"key":"e_1_3_2_2_7_1","unstructured":"Ruichu Cai Zijian Li Pengfei Wei Jie Qiao Kun Zhang and Zhifeng Hao. 2019. Learning disentangled semantic representation for domain adaptation. In IJCAI."},{"key":"e_1_3_2_2_8_1","unstructured":"Ricky TQ Chen Xuechen Li Roger Grosse and David Duvenaud. 2019. Isolating Sources of Disentanglement in VAEs. In NeurIPS."},{"key":"e_1_3_2_2_9_1","volume-title":"Variational lossy autoencoder. arXiv:1611.02731","author":"Chen Xi","year":"2016","unstructured":"Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Variational lossy autoencoder. arXiv:1611.02731 (2016)."},{"key":"e_1_3_2_2_10_1","volume-title":"A recurrent latent variable model for sequential data. arXiv preprint arXiv:1506.02216","author":"Chung Junyoung","year":"2015","unstructured":"Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, and Yoshua Bengio. 2015. A recurrent latent variable model for sequential data. arXiv preprint arXiv:1506.02216 (2015)."},{"key":"e_1_3_2_2_11_1","unstructured":"Vincent Fortuin and et al. 2018. SOM-VAE: Interpretable discrete representation learning on time series. arXiv preprint arXiv:1806.02199 (2018)."},{"key":"e_1_3_2_2_12_1","unstructured":"Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In ICML."},{"key":"e_1_3_2_2_13_1","unstructured":"Xiaojie Guo and et al. 2020. Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement. In KDD."},{"key":"e_1_3_2_2_14_1","unstructured":"Irina Higgins Loic Matthey Arka Pal Christopher Burgess Xavier Glorot Matthew Botvinick Shakir Mohamed and Alexander Lerchner. 2016. beta-vae: Learning basic visual concepts with a constrained variational framework. (2016)."},{"key":"e_1_3_2_2_15_1","volume-title":"minimum description length, and Helmholtz free energy. NeurIPS","author":"Hinton Geoffrey E","year":"1994","unstructured":"Geoffrey E Hinton and Richard S Zemel. 1994. Autoencoders, minimum description length, and Helmholtz free energy. NeurIPS (1994)."},{"key":"e_1_3_2_2_16_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation (1997)."},{"key":"e_1_3_2_2_17_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"crossref","unstructured":"Jennifer R Kwapisz and et al. 2011. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter (2011).","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_3_2_2_19_1","volume-title":"Automated Anomaly Detection via Curiosity- Guided Search and Self-Imitation Learning","author":"Li Yuening","year":"2021","unstructured":"Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu. 2021. Automated Anomaly Detection via Curiosity- Guided Search and Self-Imitation Learning. IEEE Transactions on Neural Networks and Learning Systems (2021)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00210"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358074"},{"key":"e_1_3_2_2_22_1","unstructured":"Jiayang Liu and et al. 2009. uWave: Accelerometer-based personalized gesture recognition and its applications. Pervasive and Mobile Computing (2009)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220003"},{"key":"e_1_3_2_2_24_1","volume-title":"Adversarial autoencoders. arXiv preprint arXiv:1511.05644","author":"Makhzani Alireza","year":"2015","unstructured":"Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2015. Adversarial autoencoders. arXiv preprint arXiv:1511.05644 (2015)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Arthur J Moss and et al. 1995. ECG T-wave patterns in genetically distinct forms of the hereditary long QT syndrome. Circulation (1995).","DOI":"10.1161\/01.CIR.92.10.2929"},{"key":"e_1_3_2_2_26_1","unstructured":"Sanjay Purushotham Wilka Carvalho Tanachat Nilanon and Yan Liu. 2017. Variational Recurrent Adversarial Deep Domain Adaptation. In ICLR."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Stanislau Semeniuta and et al. 2017. A hybrid convolutional variational autoencoder for text generation. arXiv preprint arXiv:1702.02390 (2017).","DOI":"10.18653\/v1\/D17-1066"},{"key":"e_1_3_2_2_28_1","volume-title":"Controlvae: Controllable variational autoencoder. In ICML.","author":"Shao Huajie","year":"2020","unstructured":"Huajie Shao and et al. 2020. Controlvae: Controllable variational autoencoder. In ICML."},{"key":"e_1_3_2_2_29_1","volume-title":"Interfacegan: Interpreting the disentangled face representation learned by gans. TPAMI","author":"Shen Yujun","year":"2020","unstructured":"Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. 2020. Interfacegan: Interpreting the disentangled face representation learned by gans. TPAMI (2020)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Allan Stisen and et al. 2015. Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition. In SenSys.","DOI":"10.1145\/2809695.2809718"},{"key":"e_1_3_2_2_31_1","unstructured":"Eric Tzeng and et al. 2014. Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)."},{"key":"e_1_3_2_2_32_1","volume-title":"Riemannian normalizing flow on variational wasserstein autoencoder for text modeling. arXiv preprint arXiv:1904.02399","author":"Wang Prince Zizhuang","year":"2019","unstructured":"Prince Zizhuang Wang and William Yang Wang. 2019. Riemannian normalizing flow on variational wasserstein autoencoder for text modeling. arXiv preprint arXiv:1904.02399 (2019)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Zirui Wang and et al. 2019. Characterizing and avoiding negative transfer. In CVPR.","DOI":"10.1109\/CVPR.2019.01155"},{"key":"e_1_3_2_2_34_1","volume-title":"Information theoretical analysis of multivariate correlation. IBM Journal of research and development","author":"Watanabe Satosi","year":"1960","unstructured":"Satosi Watanabe. 1960. Information theoretical analysis of multivariate correlation. IBM Journal of research and development (1960)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Garrett Wilson and et al. 2020. Multi-source deep domain adaptation with weak supervision for time-series sensor data. In KDD.","DOI":"10.1145\/3394486.3403228"},{"key":"e_1_3_2_2_36_1","volume-title":"Infovae: Balancing learning and inference in variational autoencoders. In AAAI.","author":"Zhao Shengjia","year":"2019","unstructured":"Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2019. Infovae: Balancing learning and inference in variational autoencoders. In AAAI."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539140","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539140","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:58Z","timestamp":1750186978000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":36,"alternative-id":["10.1145\/3534678.3539140","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539140","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}