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Universal time-series representation learning: A survey
Time-series data exists in every corner of real-world systems and services, ranging from
satellites in the sky to wearable devices on human bodies. Learning representations by …
satellites in the sky to wearable devices on human bodies. Learning representations by …
Weakly-supervised temporal action localization with multi-modal plateau Transformers
Abstract Weakly Supervised Temporal Action Localization (WSTAL) aims to jointly localize
and classify action segments in untrimmed videos with only video level annotations. To …
and classify action segments in untrimmed videos with only video level annotations. To …
Breaking the time-frequency granularity discrepancy in time-series anomaly detection
In light of the remarkable advancements made in time-series anomaly detection (TSAD),
recent emphasis has been placed on exploiting the frequency domain as well as the time …
recent emphasis has been placed on exploiting the frequency domain as well as the time …
Exploiting Representation Curvature for Boundary Detection in Time Series
Boundaries are the timestamps at which a class in a time series changes. Recently,
representation-based boundary detection has gained popularity, but its emphasis on …
representation-based boundary detection has gained popularity, but its emphasis on …
Context consistency regularization for label sparsity in time series
Labels are typically sparse in real-world time series due to the high annotation cost.
Recently, consistency regularization techniques have been used to generate artificial labels …
Recently, consistency regularization techniques have been used to generate artificial labels …
Label propagation techniques for artifact detection in imbalanced classes using photoplethysmogram signals
This study aimed to investigate the application of label propagation techniques to propagate
labels among photoplethysmogram (PPG) signals, particularly in imbalanced class …
labels among photoplethysmogram (PPG) signals, particularly in imbalanced class …
Online Drift Detection with Maximum Concept Discrepancy
Continuous learning from an immense volume of data streams becomes exceptionally
critical in the internet era. However, data streams often do not conform to the same …
critical in the internet era. However, data streams often do not conform to the same …
VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in Periodic Time Series Forecasting
Variate tokenization, which independently embeds each variate as separate tokens, has
achieved remarkable improvements in multivariate time series forecasting. However …
achieved remarkable improvements in multivariate time series forecasting. However …
Representation-based time series label propagation for active learning
D Chen, XY Li, A Li, YB Yang - 2023 26th International …, 2023 - ieeexplore.ieee.org
Time series data are ubiquitous and informative nowadays, but the labels are difficult to
obtain. Active learning is one way to reduce labeling efforts. The label-continuous property …
obtain. Active learning is one way to reduce labeling efforts. The label-continuous property …
Multivariate time series anomaly detection method based on mTranAD
Multivariate time series anomaly detection is a crucial area of research in several domains,
including finance, logistics, and manufacturing. Successfully identifying abnormal behaviors …
including finance, logistics, and manufacturing. Successfully identifying abnormal behaviors …