7 papers
To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series
Jiaming Ma, Siyuan Mu, Ruilin Tang +6
The prevailing Direct Forecasting (DF) paradigm dominates Long-term Time Series Forecasting (LTSF) by forcing models to predict the entire future horizon in a single forward pass.…
A General ReLearner: Empowering Spatiotemporal Prediction by Re-learning Input-label Residual
Jiaming Ma, Binwu Wang, Pengkun Wang +3
Prevailing spatiotemporal prediction models typically operate under a forward (unidirectional) learning paradigm, in which models extract spatiotemporal features from historical ob…
Rethinking Crystal Symmetry Prediction: A Decoupled Perspective
Liheng Yu, Zhe Zhao, Xucong Wang +2
Efficiently and accurately determining the symmetry is a crucial step in the structural analysis of crystalline materials. Existing methods usually mindlessly apply deep learning m…
Spatiotemporal Causal Decoupling Model for Air Quality Forecasting
Jiaming Ma, Guanjun Wang, Sheng Huang +4
Due to the profound impact of air pollution on human health, livelihoods, and economic development, air quality forecasting is of paramount significance. Initially, we employ the c…
Soft causal learning for generalized molecule property prediction: An environment perspective
Limin Li, Kuo Yang, Wenjie Du +3
Learning on molecule graphs has become an increasingly important topic in AI for science, which takes full advantage of AI to facilitate scientific discovery. Existing solutions on…
From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolution
Zhe Zhao, Haibin Wen, Pengkun Wang +8
Large language models (LLMs) have greatly accelerated the automation of algorithm generation and optimization. However, current methods such as EoH and FunSearch mainly rely on pre…