6 papers
LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning
Xinrui He, Qiyu Kang, Xuhao Li +1
Spiking Neural Networks (SNNs) are well-regarded for their biological plausibility and energy efficiency in processing sequential data. However, dominant SNN architectures typicall…
Fractional-order Spiking Neural Network
Chengjie Ge, Yufeng Peng, Zihao Li +6
Spiking Neural Networks (SNNs) draw inspiration from biological neurons to enable brain-like computation, demonstrating effectiveness in processing temporal information with energy…
Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
Yanan Zhao, Feng Ji, Kai Zhao +6
Graph Contrastive Learning (GCL) has recently made progress as an unsupervised graph representation learning paradigm. GCL approaches can be categorized into augmentation-based and…
Efficient Training of Neural Fractional-Order Differential Equation via Adjoint Backpropagation
Qiyu Kang, Xuhao Li, Kai Zhao +4
Fractional-order differential equations (FDEs) enhance traditional differential equations by extending the order of differential operators from integers to real numbers, offering g…
Neural Variable-Order Fractional Differential Equation Networks
Wenjun Cui, Qiyu Kang, Xuhao Li +4
Neural differential equation models have garnered significant attention in recent years for their effectiveness in machine learning applications.Among these, fractional differentia…
Distributed-Order Fractional Graph Operating Network
Kai Zhao, Xuhao Li, Qiyu Kang +5
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fraction…