Contrast everything: A hierarchical contrastive framework for medical time-series
Y Wang, Y Han, H Wang… - Advances in Neural …, 2024 - proceedings.neurips.cc
Contrastive representation learning is crucial in medical time series analysis as it alleviates
dependency on labor-intensive, domain-specific, and scarce expert annotations. However …
dependency on labor-intensive, domain-specific, and scarce expert annotations. However …
Self-supervised contrastive learning for medical time series: A systematic review
Medical time series are sequential data collected over time that measures health-related
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive …
SimTS: rethinking contrastive representation learning for time series forecasting
X Zheng, X Chen, M Schürch, A Mollaysa… - arXiv preprint arXiv …, 2023 - arxiv.org
Contrastive learning methods have shown an impressive ability to learn meaningful
representations for image or time series classification. However, these methods are less …
representations for image or time series classification. However, these methods are less …
Som-vae: Interpretable discrete representation learning on time series
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 …
optimized to work well in high-dimensional spaces, these areas could benefit from …
MHCCL: masked hierarchical cluster-wise contrastive learning for multivariate time series
Learning semantic-rich representations from raw unlabeled time series data is critical for
downstream tasks such as classification and forecasting. Contrastive learning has recently …
downstream tasks such as classification and forecasting. Contrastive learning has recently …
Time-series representation learning via temporal and contextual contrasting
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 …
a very challenging task. In this paper, we propose an unsupervised Time-Series …
Utilizing expert features for contrastive learning of time-series representations
MT Nonnenmacher, L Oldenburg… - International …, 2022 - proceedings.mlr.press
We present an approach that incorporates expert knowledge for time-series representation
learning. Our method employs expert features to replace the commonly used data …
learning. Our method employs expert features to replace the commonly used data …
Time series contrastive learning with information-aware augmentations
Various contrastive learning approaches have been proposed in recent years and achieve
significant empirical success. While effective and prevalent, contrastive learning has been …
significant empirical success. While effective and prevalent, contrastive learning has been …
Improving clinical predictions through unsupervised time series representation learning
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 …
which bears the promise of leveraging copious amounts of existing unlabeled data in order …
Finding order in chaos: A novel data augmentation method for time series in contrastive learning
BU Demirel, C Holz - Advances in Neural Information …, 2024 - proceedings.neurips.cc
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 …
Although the degree of data augmentations has been well controlled by utilizing pre-defined …