[45] Douglas Steinley. Properties of the hubert-arable adjusted rand index. Psychological methods, 9(3):386, 2004.
[46] Timo Sztyler and Heiner Stuckenschmidt. On-body localization of wearable devices: An investigation of position-aware activity recognition. In IEEE International Conference on Pervasive Computing and Communications (PerCom), 2016.
[47] Aiham Taleb, Matthias Kirchler, Remo Monti, and Christoph Lippert. Contig: Self-supervised multimodal contrastive learning for medical imaging with genetics. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
[48] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. In European Conference on Computer Vision (ECCV), 2020.
[49] Sana Tonekaboni, Danny Eytan, and Anna Goldenberg. Unsupervised representation learning for time series with temporal neighborhood coding. In International Conference on Learning Representations (ICLR), 2021.
[50] Xinming Tu, Zhi-Jie Cao, Sara Mostafavi, Ge Gao, et al. Cross-linked unified embedding for cross-modality representation learning. Advances in Neural Information Processing Systems (NeurIPS), 2022.
[51] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research (JMLR), 9(11), 2008.
[52] Feng Wang and Huaping Liu. Understanding the behaviour of contrastive loss. In IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[53] Tongzhou Wang and Phillip Isola. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In International Conference on Machine Learning (ICML), 2020.
[54] Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshit Kumar, and Steven Hoi. CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting. In International Conference on Learning Representations (ICLR), 2022.
[55] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[56] Han Xu, Zheng Yang, Zimu Zhou, Longfei Shangguan, Ke Yi, and Yunhao Liu. Indoor localization via multi-modal sensing on smartphones. In ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2016.
[57] Yihao Xue, Kyle Whitecross, and Baharan Mirzasoleiman. Investigating why contrastive learning benefits robustness against label noise. In International Conference on Machine Learning (ICML), pages 24851-24871, 2022.
[58] Xinyu Yang, Zhenguo Zhang, and Rongyi Cui. Timeclr: A self-supervised contrastive learning framework for univariate time series representation. Knowledge-Based Systems, 245:108606, 2022.
[59] Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In International Conference on World Wide Web (WWW), 2017.
[60] Shuochao Yao, Yiran Zhao, Huajie Shao, Dongxin Liu, Shengzhong Liu, Yifan Hao, Ailing Piao, Shaohan Hu, Su Lu, and Tarek F Abdelzaher. Sadeepsense: Self-attention deep learning framework for heterogeneous on-device sensors in internet of things applications. In IEEE International Conference on Computer Communications (INFOCOM), 2019.
[61] Shuochao Yao, Yiran Zhao, Huajie Shao, Chao Zhang, Aston Zhang, Shaohan Hu, Dongxin Liu, Shengzhong Liu, Lu Su, and Tarek Abdelzaher. Sensegan: Enabling deep learning for internet of things with a semi-supervised framework. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2(3):1-21, 2018.