4 citations · 6 across the 5 of their papers we have counts for
5 papers
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
Xinlei Yu, Zhangquan Chen, Yongbo He +36
Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an inc…
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
Hao Wu, Haomin Wen, Guibin Zhang +5
The ever-increasing sensor service, though opening a precious path and providing a deluge of earth system data for deep-learning-oriented earth science, sadly introduce a daunting…
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
Kun Wang, Hao Wu, Guibin Zhang +5
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.…
Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model
Hao Wu, Yuxuan Liang, Wei Xiong +4
Efficiently modeling spatio-temporal (ST) physical processes and observations presents a challenging problem for the deep learning community. Many recent studies have concentrated…
Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training
Xinglin Li, Kun Wang, Hanhui Deng +2
In this paper, we study the \textit{graph condensation} problem by compressing the large, complex graph into a concise, synthetic representation that preserves the most essential a…