Search Results for author:
Found 10 papers, 4 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.
STAND-Guard: A Small Task-Adaptive Content Moderation Model
Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large language models.
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.
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
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).
Label-Efficient Interactive Time-Series Anomaly Detection
Time-series anomaly detection is an important task and has been widely applied in the industry.
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.
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.
Time-Series Anomaly Detection Service at Microsoft
At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time.
