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Found 7 papers, 3 papers with code
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU).
TS2Vec: Towards Universal Representation of Time Series
Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Multivariate Time-series Anomaly Detection via Graph Attention Network
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
Automated Model Selection for Time-Series Anomaly Detection
The task is challenging because of the complex characteristics of time-series, which are messy, stochastic, and often without proper labels.
Human Annotations Improve GAN Performances
Unlike previous paradigms that directly ask annotators to distinguish between real and fake data in a straightforward way, we propose and annotate a set of carefully designed attributes that encode important image information at various levels, to understand the differences between fake and real images.
SALICON: Saliency in Context
Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention.
