4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.AI2024
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies
Mingkun Xu, Huifeng Yin, Yujie Wu +5
In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biologic…
cs.AI2024
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
Huifeng Yin, Mingkun Xu, Jing Pei +1
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical c…
cs.NE2024★ 4 cited
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks
Huifeng Yin, Hanle Zheng, Jiayi Mao +6
Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is…