PDFTime reformulates multivariate time series classification as a multi-stage prototype-based decision process, claiming SOTA results on UCR and UEA benchmarks.
Guiding masked repre- sentation learning to capture spatio-temporal relationship of electrocardiogram.arXiv preprint arXiv:2402.09450
3 Pith papers cite this work. Polarity classification is still indexing.
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ER-JEPA, a lightweight two-stage Joint-Embedding Predictive Architecture that separates channel-wise and temporal processing, achieves state-of-the-art ECG classification on PTB-XL with lower compute than prior transformer models.
citing papers explorer
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Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
PDFTime reformulates multivariate time series classification as a multi-stage prototype-based decision process, claiming SOTA results on UCR and UEA benchmarks.
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Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis
ER-JEPA, a lightweight two-stage Joint-Embedding Predictive Architecture that separates channel-wise and temporal processing, achieves state-of-the-art ECG classification on PTB-XL with lower compute than prior transformer models.
- LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space