5 papers
SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
ChengAo Shen, Ziming Zhao, Hanghang Tong +4
Time series AI is crucial for analyzing dynamic web content, driving a surge of pre-trained large models known for their strong knowledge encoding and transfer capabilities across…
From Binary to Continuous: Stochastic Re-Weighting for Robust Graph Explanation
Zhuomin Chen, Jingchao Ni, Hojat Allah Salehi +2
Graph Neural Networks (GNNs) have achieved remarkable performance in a wide range of graph-related learning tasks. However, explaining their predictions remains a challenging probl…
Deep Reinforcement Learning for MIMO Communication with Low-Resolution ADCs
Marian Temprana Alonso, Dongsheng Luo, Farhad Shirani
Multiple-input multiple-output (MIMO) wireless systems conventionally use high-resolution analog-to-digital converters (ADCs) at the receiver side to faithfully digitize received s…
Harnessing Vision Models for Time Series Analysis: A Survey
Jingchao Ni, Ziming Zhao, ChengAo Shen +5
Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs).…
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
ChengAo Shen, Zhengzhang Chen, Dongsheng Luo +3
Causal discovery is an imperative foundation for decision-making across domains, such as smart health, AI for drug discovery and AIOps. Traditional statistical causal discovery met…