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Not all positive pairs are beneficial to time series contrastive learning.
Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley · 2000
Earlier work this paper cites.
Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state
Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger · 2001
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
A public domain dataset for human activity recognition using smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, and Jorge Luis Reyes Ortiz · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks
Terry T Um, Franz MJ Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kulić · 2017
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Learning with bad training data via iterative trimmed loss minimization
Yanyao Shen and Sujay Sanghavi · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Deep learning for ecg analysis: Benchmarks and insights from ptb-xl
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter, and Wojciech Samek · 2020
Cited alongside, same era.
Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi · 2020
Cited alongside, same era.
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
Cited alongside, same era.
Ptb-xl, a large publicly available electrocardiography dataset
Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I Lunze, Wojciech Samek, and Tobias Schaeffter · 2020
Cited alongside, same era.
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Later among the works it cites.
Robust contrastive learning against noisy views
Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, and Yale Song · 2022
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Intra-inter subject self-supervised learning for multivariate cardiac signals
Xiang Lan, Dianwen Ng, Shenda Hong, and Mengling Feng · 2022
Later among the works it cites.
Rethinking minimal sufficient representation in contrastive learning
Haoqing Wang, Xun Guo, Zhi-Hong Deng, and Yan Lu · 2022
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Contrastive learning with stronger augmentations
Xiao Wang and Guo-Jun Qi · 2022
Later among the works it cites.
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Time-series representation learning via temporal and contextual contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan · 2021
Cited alongside, same era.
Robust audio-visual instance discrimination
Pedro Morgado, Ishan Misra, and Nuno Vasconcelos · 2021
Cited alongside, same era.
Unsupervised representation learning for time series with temporal neighborhood coding
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg · 2021
Cited alongside, same era.
Toward understanding the feature learning process of self-supervised contrastive learning
Zixin Wen and Yuanzhi Li · 2021
Cited alongside, same era.
Neighborhood contrastive learning applied to online patient monitoring
Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch · 2021
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He
Cited in the paper.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He
Cited in the paper.
Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, and Steven Hoi · 2022
Later among the works it cites.
Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Ling Yang and Shenda Hong · 2022
Later among the works it cites.
Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, and Bixiong Xu · 2022
Later among the works it cites.
Contrastive learning for unsupervised domain adaptation of time series
Yilmazcan Ozyurt, Stefan Feuerriegel, and Ce Zhang · 2023
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