Contrastive representation learning is crucial in medical time series analysis as it alleviates
dependency on labor-intensive, domain-specific, and scarce expert annotations. However …

Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …

Contrastive learning methods have shown an impressive ability to learn meaningful
representations for image or time series classification. However, these methods are less …

High-dimensional time series are common in many domains. Since human cognition is not
optimized to work well in high-dimensional spaces, these areas could benefit from …

Learning semantic-rich representations from raw unlabeled time series data is critical for
downstream tasks such as classification and forecasting. Contrastive learning has recently …

Learning decent representations from unlabeled time-series data with temporal dynamics is
a very challenging task. In this paper, we propose an unsupervised Time-Series …

We present an approach that incorporates expert knowledge for time-series representation
learning. Our method employs expert features to replace the commonly used data …

Various contrastive learning approaches have been proposed in recent years and achieve
significant empirical success. While effective and prevalent, contrastive learning has been …

In this work, we investigate unsupervised representation learning on medical time series,
which bears the promise of leveraging copious amounts of existing unlabeled data in order …

The success of contrastive learning is well known to be dependent on data augmentation.
Although the degree of data augmentations has been well controlled by utilizing pre-defined …