AI RESEARCH
Learning Fingerprints for Medical Time Series with Redundancy-Constrained Information Maximization
arXiv CS.LG
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ArXi:2605.00130v1 Announce Type: new Learning meaningful representations from medical time series (MedTS) such as ECG or EEG signals is a critical challenge. These signals are often high-dimensional, variable-length and rife with noise. Existing self-supervised approaches, such as Masked Autoencoders (MAEs) are highly effective for pre-