87 citations · 108 across the 6 of their papers we have counts for
6 papers
Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias
Hao Wu, Yuan Gao, Chang Liu +11
Accurately predicting the long-term evolution of turbulence is crucial for advancing scientific understanding and optimizing engineering applications. However, existing deep learni…
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Kun Wang, Guibin Zhang, Zhenhong Zhou +100
The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…
Position: LLMs Can be Good Tutors in English Education
Jingheng Ye, Shen Wang, Deqing Zou +8
While recent efforts have begun integrating large language models (LLMs) into English education, they often rely on traditional approaches to learning tasks without fully embracing…
Deep Learning for Multivariate Time Series Imputation: A Survey
Jun Wang, Wenjie Du, Yiyuan Yang +6
Missing values are ubiquitous in multivariate time series (MTS) data, posing significant challenges for accurate analysis and downstream applications. In recent years, deep learnin…
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models
Zefan Wang, Zichuan Liu, Yingying Zhang +6
Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently. However, current methods are still reliant on manual workflow setti…
Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting
Ming Jin, Guangsi Shi, Yuan-Fang Li +7
Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural ne…