Fetching the paper…
Reading the bibliography…
Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision.
The TUH EEG CORPUS: A big data resource for automated EEG interpretation
Harati, A.; Lopez, S.; Obeid, I.; Picone, J.; Jacobson, M.; and Tobochnik, S. 2014 · 2014
Earlier work this paper cites.
Neural discrete representation learning
Van Den Oord, A.; Vinyals, O.; et al. 2017 · 2017
Earlier work this paper cites.
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Bai, S.; Kolter, J. Z.; and Koltun, V. 2018 · 2018
Earlier work this paper cites.
Som-vae: Interpretable discrete representation learning on time series
Fortuin, V.; Hüser, M.; Locatello, F.; Strathmann, H.; and Rätsch, G. 2018 · 2018
Earlier work this paper cites.
EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces
Lawhern, V. J.; Solon, A. J.; Waytowich, N. R.; Gordon, S. M.; Hung, C. P.; and Lance, B. J. 2018 · 2018
Earlier work this paper cites.
Unsupervised scalable representation learning for multivariate time series
Franceschi, J.-Y.; Dieuleveut, A.; and Jaggi, M. 2019 · 2019
Earlier work this paper cites.
A dataset of neonatal EEG recordings with seizure annotations
Stevenson, N. J.; Tapani, K.; Lauronen, L.; and Vanhatalo, S. 2019 · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
Earlier work this paper cites.
Contrastive representation learning for electroencephalogram classification
Mohsenvand, M. N.; Izadi, M. R.; and Maes, P. 2020 · 2020
Earlier work this paper cites.
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. 2021 · 2021
Earlier work this paper cites.
Taming transformers for high-resolution image synthesis
Esser, P.; Rombach, R.; and Ommer, B. 2021 · 2021
Earlier work this paper cites.
Self-supervised contrastive learning for EEG-based sleep staging
Jiang, X.; Zhao, J.; Du, B.; and Yuan, Z. 2021 · 2021
Cited alongside, same era.
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Kostas, D.; Aroca-Ouellette, S.; and Rudzicz, F. 2021 · 2021
Cited alongside, same era.
A multi-domain adaptive graph convolutional network for EEG-based emotion recognition
Li, R.; Wang, Y.; and Lu, B.-L. 2021 · 2021
Cited alongside, same era.
Variational instance-adaptive graph for EEG emotion recognition
Song, T.; Liu, S.; Zheng, W.; Zong, Y.; Cui, Z.; Li, Y.; and Zhou, X. 2021 · 2021
Cited alongside, same era.
Self-supervised graph neural networks for improved electroencephalographic seizure analysis
Tang, S.; Dunnmon, J. A.; Saab, K.; Zhang, X.; Huang, Q.; Dubost, F.; Rubin, D. L.; and Lee-Messer, C. 2021 · 2021
Cited alongside, same era.
Hierarchical dynamic graph convolutional network with interpretability for EEG-based emotion recognition
Ye, M.; Chen, C. P.; and Zhang, T. 2022 · 2022
Later among the works it cites.
Ts2vec: Towards universal representation of time series
Yue, Z.; Wang, Y.; Duan, J.; Yang, T.; Huang, C.; Tong, Y.; and Xu, B. 2022 · 2022
Later among the works it cites.
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
Darvishi-Bayazi, M.-J.; Ghaemi, M. S.; Lesort, T.; Arefin, M. R.; Faubert, J.; and Rish, I. 2023 · 2023
Later among the works it cites.
Large language models are zero-shot time series forecasters
Gruver, N.; Finzi, M.; Qiu, S.; and Wilson, A. G. 2023 · 2023
Later among the works it cites.
Clinically Relevant Unsupervised Online Representation Learning of ICU Waveforms
Gulamali, F. F.; Sawant, A. S.; Hofer, I.; Levin, M.; Singh, K.; Glicksberg, B. S.; and Nadkarni, G. N. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unsupervised representation learning for time series with temporal neighborhood coding
Tonekaboni, S.; Eytan, D.; and Goldenberg, A. 2021 · 2021
Cited alongside, same era.
XAI for transformers: Better explanations through conservative propagation
Ali, A.; Schnake, T.; Eberle, O.; Montavon, G.; Müller, K.-R.; and Wolf, L. 2022 · 2022
Cited alongside, same era.
Temporal dependencies in feature importance for time series prediction
Leung, K. K.; Rooke, C.; Smith, J.; Zuberi, S.; and Volkovs, M. 2022 · 2022
Cited alongside, same era.
A Multi-view Spectral-Spatial-Temporal Masked Autoencoder for Decoding Emotions with Self-supervised Learning
Li, R.; Wang, Y.; Zheng, W.-L.; and Lu, B.-L. 2022 · 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 · 2022
Cited alongside, same era.
Beit v2: Masked image modeling with vector-quantized visual tokenizers
Peng, Z.; Dong, L.; Bao, H.; Ye, Q.; and Wei, F. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
Li, Z.; Fang, Y.; Li, Y.; Ren, K.; Wang, Y.; Luo, X.; Duan, J.; Huang, C.; Li, D.; and Qiu, L. 2023 · 2023
Later among the works it cites.
Labeling EEG Components with a Bag of Waveforms from Learned Dictionaries
Mendoza-Cardenas, C. H.; Meek, A.; and Brockmeier, A. J. 2023 · 2023
Later among the works it cites.
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Tang, S.; Dunnmon, J. A.; Liangqiong, Q.; Saab, K. K.; Baykaner, T.; Lee-Messer, C.; and Rubin, D. L. 2023 · 2023
Later among the works it cites.
BrainBERT: Self-supervised representation learning for intracranial recordings
Wang, C.; Subramaniam, V.; Yaari, A. U.; Kreiman, G.; Katz, B.; Cases, I.; and Barbu, A. 2023 · 2023
Later among the works it cites.
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
Yi, K.; Wang, Y.; Ren, K.; and Li, D. 2023 · 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 · 2023
Later among the works it cites.