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Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks.
Signature verification using a” siamese” time delay neural network
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., and Shah, R · 1993
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Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E · 2000
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Analysis of electroencephalograms in alzheimer’s disease patients with multiscale entropy
Escudero, J., Abásolo, D., Hornero, R., Espino, P., and López, M · 2006
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Bai, S., Kolter, J. Z., and Koltun, V · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Lai, G., Chang, W.-C., Yang, Y., and Liu, H · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Unsupervised scalable representation learning for multivariate time series
Franceschi, J.-Y., Dieuleveut, A., and Jaggi, M · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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One fits all: Power general time series analysis by pretrained lm
Zhou, T., Niu, P., Wang, X., Sun, L., and Jin, R · 2019
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Exploring contrastive learning in human activity recognition for healthcare
Tang, C. I., Perez-Pozuelo, I., Spathis, D., and Mascolo, C · 2020
Cited alongside, same era.
Time series data augmentation for deep learning: A survey
Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., and Xu, H · 2020
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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The two decades brainclinics research archive for insights in neurophysiology (tdbrain) database
Van Dijk, H., Van Wingen, G., Denys, D., Olbrich, S., Van Ruth, R., and Arns, M · 2022
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Learning latent seasonal-trend representations for time series forecasting
Wang, Z., Xu, X., Zhang, W., Trajcevski, G., Zhong, T., and Zhou, F · 2022
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Mixing up contrastive learning: Self-supervised representation learning for time series
Wickstrøm, K., Kampffmeyer, M., Mikalsen, K. Ø., and Jenssen, R · 2022
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Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting
Woo, G., Liu, C., Sahoo, D., Kumar, A., and Hoi, S · 2022
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Chen, X. and He, K · 2021
Cited alongside, same era.
Simcse: Simple contrastive learning of sentence embeddings
Gao, T., Yao, X., and Chen, D · 2021
Cited alongside, same era.
Self-supervised learning: Generative or contrastive
Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., and Tang, J · 2021
Cited alongside, same era.
Unsupervised representation learning for time series with temporal neighborhood coding
Tonekaboni, S., Eytan, D., and Goldenberg, A · 2021
Cited alongside, same era.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H., Xu, J., Wang, J., and Long, M · 2021
Cited alongside, same era.
What should not be contrastive in contrastive learning
Xiao, T., Wang, X., Efros, A. A., and Darrell, T · 2021
Cited alongside, same era.
A transformer-based framework for multivariate time series representation learning
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., and Eickhoff, C · 2021
Cited alongside, same era.
Simmim: A simple framework for masked image modeling
Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., and Hu, H · 2022
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TS2Vec: Towards Universal Representation of Time Series
Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., and Xu, B · 2022
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Self-supervised contrastive pre-training for time series via time-frequency consistency
Zhang, X., Zhao, Z., Tsiligkaridis, T., and Zitnik, M · 2022
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Simmtm: A simple pre-training framework for masked time-series modeling
Dong, J., Wu, H., Zhang, H., Zhang, L., Wang, J., and Long, M · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2023
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Contrast everything: A hierarchical contrastive framework for medical time-series
Wang, Y., Han, Y., Wang, H., and Zhang, X · 2023
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itransformer: Inverted transformers are effective for time series forecasting
Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., and Long, M · 2024
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Himtm: Hierarchical multi-scale masked time series modeling for long-term forecasting
Zhao, S., Jin, M., Hou, Z., Yang, C., Li, Z., Wen, Q., and Wang, Y · 2024
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