2 citations · 3 across the 3 of their papers we have counts for
4 papers
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery
Xinzhe Yuan, Zimu Zeng, Zhuo Chen +6
Mixed-variable optimization in scientific discovery often requires exploring large combinatorial spaces under costly experimental evaluations, making sample?efficient Bayesian opti…
Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
Xinzhe Yuan, Zhuo Chen, Jianshu Zhang +4
Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for science. Bayesian Optimizat…
Continuous Spiking Graph Neural Networks
Nan Yin, Shiqi Fan, Mengzhu Wan +10
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing contin…
Dynamic Spiking Framework for Graph Neural Networks
Nan Yin, Mengzhu Wang, Zhenghan Chen +3
The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in process…