3 papers
cs.AI2026
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…
cs.NE2025
Continuous Spiking Graph Neural Networks
Nan Yin, Mengzhu Wan, Li Shen +4
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing contin…
cs.NE2025
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…