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Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classification, and machine fault diagnosis.
Language models are few-shot learners
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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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The information bottleneck method
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Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., and Moore, S. A · 2011
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A public domain dataset for human activity recognition using smartphones
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J. L., et al · 2013
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Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition
Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T. S., Kjærgaard, M. B., Dey, A., Sonne, T., and Jensen, M. M · 2015
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Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification
Lessmeier, C., Kimotho, J. K., Zimmer, D., and Sextro, W · 2016
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Variational recurrent adversarial deep domain adaptation
Purushotham, S., Carvalho, W., Nilanon, T., and Liu, Y · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, B., and Saenko, K · 2016
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Learning sleep stages from radio signals: A conditional adversarial architecture
Zhao, M., Yue, S., Katabi, D., Jaakkola, T. S., and Bianchi, M. T · 2017
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Tinet: learning invariant networks via knowledge transfer
Luo, C., Chen, Z., Tang, L.-A., Shrivastava, A., Li, Z., Chen, H., and Ye, J · 2018
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On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
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A dirt-t approach to unsupervised domain adaptation
Shu, R., Bui, H., Narui, H., and Ermon, S · 2018
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Multi-instance domain adaptation for vaccine adverse event detection
Wang, J., and Zhao, L · 2018
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Domain agnostic learning with disentangled representations
Peng, X., Huang, Z., Sun, X., and Saenko, K · 2019
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On variational bounds of mutual information
Poole, B., Ozair, S., Van Den Oord, A., Alemi, A., and Tucker, G · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Ying, Z., Bourgeois, D., You, J., Zitnik, M., and Leskovec, J · 2019
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Club: A contrastive log-ratio upper bound of mutual information
Cheng, P., Hao, W., Dai, S., Liu, J., Gan, Z., and Carin, L · 2020
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On minimum discrepancy estimation for deep domain adaptation
Rahman, M. M., Fookes, C., Baktashmotlagh, M., and Sridharan, S · 2020
Cited alongside, same era.
Multi-source deep domain adaptation with weak supervision for time-series sensor data
Wilson, G., Doppa, J. R., and Cook, D. J · 2020
Cited alongside, same era.
Multi-source domain adaptation in the deep learning era: A systematic survey
Zhao, S., Li, B., Xu, P., and Keutzer, K · 2020
Cited alongside, same era.
Deep subdomain adaptation network for image classification
Zhu, Y., Zhuang, F., Wang, J., Ke, G., Chen, J., Bian, J., Xiong, H., and He, Q · 2020
Cited alongside, same era.
Time series domain adaptation via sparse associative structure alignment
Cai, R., Chen, J., Li, Z., Chen, W., Zhang, K., Ye, J., Li, Z., Yang, X., and Zhang, Z · 2021
Cited alongside, same era.
Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms
Chang, C., Peng, W.-C., and Chen, T.-F · 2023
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Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G · 2023
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Domain adaptation for time series under feature and label shifts
He, H., Queen, O., Koker, T., Cuevas, C., Tsiligkaridis, T., and Zitnik, M · 2023
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Time-llm: Time series forecasting by reprogramming large language models
Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., et al · 2023
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Large models for time series and spatio-temporal data: A survey and outlook
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Lester, B., Al-Rfou, R., and Constant, N · 2021
Cited alongside, same era.
Adversarial spectral kernel matching for unsupervised time series domain adaptation
Liu, Q., and Xue, H · 2021
Cited alongside, same era.
Temporal domain generalization with drift-aware dynamic neural networks
Bai, G., Ling, C., and Zhao, L · 2022
Cited alongside, same era.
Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
Fedus, W., Zoph, B., and Shazeer, N · 2022
Cited alongside, same era.
Domain adaptation for time series forecasting via attention sharing
Jin, X., Park, Y., Maddix, D., Wang, H., and Wang, Y · 2022
Cited alongside, same era.
Towards learning disentangled representations for time series
Li, Y., Chen, Z., Zha, D., Du, M., Ni, J., Zhang, D., Chen, H., and Hu, X · 2022
Cited alongside, same era.
A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2022
Cited alongside, same era.
Jin, M., Wen, Q., Liang, Y., Zhang, C., Xue, S., Wang, X., Zhang, J., Wang, Y., Chen, H., Li, X., Pan, S., Tseng, V. S., Zheng, Y., Chen, L., and Xiong, H · 2023
Closest in time.
Context-aware domain adaptation for time series anomaly detection
Lai, K.-H., Wang, L., Chen, H., Zhou, K., Wang, F., Yang, H., and Hu, X · 2023
Closest in time.
Domain specialization as the key to make large language models disruptive: A comprehensive survey
Ling, C., Zhao, X., Lu, J., Deng, C., Zheng, C., Wang, J., Chowdhury, T., Li, Y., Cui, H., Zhao, T., et al · 2023
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Time series contrastive learning with information-aware augmentations
Luo, D., Cheng, W., Wang, Y., Xu, D., Ni, J., Yu, W., Zhang, X., Liu, Y., Chen, Y., Chen, H., et al · 2023
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Adatime: A benchmarking suite for domain adaptation on time series data
Ragab, M., Eldele, E., Tan, W. L., Foo, C.-S., Chen, Z., Wu, M., Kwoh, C.-K., and Li, X · 2023
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Test: Text prototype aligned embedding to activate llm’s ability for time series
Sun, C., Li, Y., Li, H., and Hong, S · 2023
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Incremental causal graph learning for online root cause analysis
Wang, D., Chen, Z., Fu, Y., Liu, Y., and Chen, H · 2023
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Interdependent causal networks for root cause localization
Wang, D., Chen, Z., Ni, J., Tong, L., Wang, Z., Fu, Y., and Chen, H · 2023
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Universality and limitations of prompt tuning
Wang, Y., Chauhan, J., Wang, W., and Hsieh, C.-J · 2023
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Promptcast: A new prompt-based learning paradigm for time series forecasting
Xue, H., and Salim, F. D · 2023
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A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al · 2023
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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 · 2023
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Continuous temporal domain generalization
Cai, Z., Bai, G., Jiang, R., Song, X., and Zhao, L · 2024
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