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Found 9 papers, 3 papers with code
Can Open-source LLMs Enhance Data Synthesis for Toxic Detection?: An Experimental Study
This study explores the potential of open-source LLMs for harmful data synthesis, utilizing prompt engineering and fine-tuning techniques to enhance data quality and diversity.
ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information
In different NLP tasks, detecting harmful content is crucial for online environments, especially with the growing influence of social media.
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.
Ranked #8 on
Traffic Prediction
on EXPY-TKY
Graph Neural Network
Multivariate Time Series Forecasting
+2
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.
Ranked #4 on
Unsupervised Anomaly Detection
on SMAP
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.
