4 papers
Coarse-to-Fine Learning of Dynamic Causal Structures
Dezhi Yang, Qiaoyu Tan, Carlotta Domeniconi +3
Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal…
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
Yu-Neng Chuang, Songchen Li, Jiayi Yuan +11
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time…
Gradient Rewiring for Editable Graph Neural Network Training
Zhimeng Jiang, Zirui Liu, Xiaotian Han +6
Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks…
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
Yuyan Liu, Sirui Ding, Sheng Zhou +2
Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery. However, prior methods are limited by the requirement for a large number of labeled molecul…