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Data Augmentation for Electrocardiograms.

, , , , and . CHIL, volume 174 of Proceedings of Machine Learning Research, page 282-310. PMLR, (2022)

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Mollack: a web server for the automated creation of conformational ensembles for intrinsically disordered proteins., , , , and . Bioinform., 32 (16): 2545-2547 (2016)Data Augmentation for Electrocardiograms., , , , and . CHIL, volume 174 of Proceedings of Machine Learning Research, page 282-310. PMLR, (2022)Generative Oversampling with a Contrastive Variational Autoencoder., , , , , and . ICDM, page 101-109. IEEE, (2019)QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG., , and . EMBC, page 1-4. IEEE, (2023)Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge., , and . EURASIP J. Adv. Signal Process., (2007)Efficient Construction of Disordered Protein Ensembles in a Bayesian Framework with Optimal Selection of Conformations., , and . Pacific Symposium on Biocomputing, page 82-93. World Scientific Publishing, (2012)Quantifying Common Support between Multiple Treatment Groups Using a Contrastive-VAE., and . ML4H@NeurIPS, volume 136 of Proceedings of Machine Learning Research, page 41-52. PMLR, (2020)Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 28531-28548. PMLR, (2023)Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals., , and . MLHC, volume 219 of Proceedings of Machine Learning Research, page 321-342. PMLR, (2023)Learning to predict with supporting evidence: applications to clinical risk prediction., , , , , and . CHIL, page 95-104. ACM, (2021)