Fetching the paper…
Reading the bibliography…
In natural language processing and vision, pretraining is utilized to learn effective representations.
Dynamic programming algorithm optimization for spoken word recognition
Sakoe, H. and Chiba, S · 1978
Earlier work this paper cites.
A Cluster Separation Measure
Davies, D. L. and Bouldin, D. W · 1979
Earlier work this paper cites.
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J · 2006
Earlier work this paper cites.
Improved Deep Metric Learning with Multi-class N-pair Loss Objective
Sohn, K · 2016
Earlier work this paper cites.
Attention is All you Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Time series classification from scratch with deep neural networks: A strong baseline
Wang, Z., Yan, W., and Oates, T · 2017
Earlier work this paper cites.
The M4 Competition: Results, findings, conclusion and way forward
Makridakis, S., Spiliotis, E., and Assimakopoulos, V · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
van den Oord, A., Li, Y., and Vinyals, O · 2018
Earlier work this paper cites.
mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
Earlier work this paper cites.
Optuna: A Next-generation Hyperparameter Optimization Framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M · 2019
Earlier work this paper cites.
The UCR time series archive
Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C.-C. M., Zhu, Y., Gharghabi, S., Ratanamahatana, C. A., and Keogh, E · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Earlier work this paper cites.
Unsupervised scalable representation learning for multivariate time series
Franceschi, J.-Y., Dieuleveut, A., and Jaggi, M · 2019
Earlier work this paper cites.
ConvTimeNet: A pre-trained deep convolutional neural network for time series classification
Kashiparekh, K., Narwariya, J., Malhotra, P., Vig, L., and Shroff, G · 2019
Earlier work this paper cites.
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., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Earlier work this paper cites.
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Thulasidasan, S., Chennupati, G., Bilmes, J. A., Bhattacharya, T., and Michalak, S · 2019
Cited alongside, same era.
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
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. E · 2020
Cited alongside, same era.
Time-Series Representation Learning via Temporal and Contextual Contrasting
Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., and Guan, C · 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
TS2Vec: Towards Universal Representation of Time Series
Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., and Xu, B · 2022
Later among the works it cites.
Self-supervised contrastive pre-training for time series via time-frequency consistency
Zhang, X., Zhao, Z., Tsiligkaridis, T., and Zitnik, M · 2022
Later among the works it cites.
PAITS: Pretraining and augmentation for irregularly-sampled time series
Beebe-Wang, N., Ebrahimi, S., Yoon, J., Arik, S. O., and Pfister, T · 2023
Later among the works it cites.
Multi-view self-supervised learning for multivariate variable-channel time series
Brüsch, T., Schmidt, M. N., and Alstrøm, T. S · 2023
Later among the works it cites.
TimeMAE: Self-supervised representations of time series with decoupled masked autoencoders
Cheng, M., Liu, Q., Liu, Z., Zhang, H., Zhang, R., and Chen, E · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding
Tonekaboni, S., Eytan, D., and Goldenberg, A · 2020
Cited alongside, same era.
Homogeneous Transfer Active Learning for Time Series Classification
Gikunda, P. and Jouandeau, N · 2021
Cited alongside, same era.
An empirical survey of data augmentation for time series classification with neural networks
Iwana, B. K. and Uchida, S · 2021
Cited alongside, same era.
CLOCS: Contrastive learning of cardiac signals across space, time, and patients
Kiyasseh, D., Zhu, T., and Clifton, D. A · 2021
Cited alongside, same era.
Self-Supervised Pre-training for Time Series Classification
Shi, P., Ye, W., and Qin, Z · 2021
Cited alongside, same era.
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Yang, C.-H. H., Tsai, Y.-Y., and Chen, P.-Y · 2021
Cited alongside, same era.
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Cited alongside, same era.
Later among the works it cites.
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
Later among the works it cites.
Large Language Models Are Zero-Shot Time Series Forecasters
Gruver, N., Finzi, M. A., Qiu, S., and Wilson, A. G · 2023
Later among the works it cites.
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., and Wen, Q · 2023
Later among the works it cites.
Ti-MAE: Self-supervised masked time series autoencoders
Li, Z., Rao, Z., Pan, L., Wang, P., and Xu, Z · 2023
Later among the works it cites.
A survey on time-series pre-trained models
Ma, Q., Liu, Z., Zheng, Z., Huang, Z., Zhu, S., Yu, Z., and Kwok, J. T · 2023
Later among the works it cites.
A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2023
Later among the works it cites.
Lightweight, pre-trained transformers for remote sensing timeseries
Tseng, G., Zvonkov, I., Purohit, M., Rolnick, D., and Kerner, H · 2023
Later among the works it cites.
Are Transformers Effective for Time Series Forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q · 2023
Later among the works it cites.
One Fits All: Power General Time Series Analysis by Pretrained LM
Zhou, T., Niu, P., Wang, X., Sun, L., and Jin, R · 2023
Later among the works it cites.