19 citations · 24 across the 10 of their papers we have counts for
4 papers · 1 filter
The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting
Lefei Shen, Mouxiang Chen, Han Fu +5
Transformer-based models have recently become dominant in Long-term Time Series Forecasting (LTSF), yet the variations in their architecture, such as encoder-only, encoder-decoder,…
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
Qiheng Mao, Zemin Liu, Chenghao Liu +2
The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harn…
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
Mouxiang Chen, Zemin Liu, Chenghao Liu +3
Recent research has demonstrated the efficacy of pre-training graph neural networks (GNNs) to capture the transferable graph semantics and enhance the performance of various downst…
Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
Mouxiang Chen, Lefei Shen, Han Fu +3
Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are s…