8 papers
Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks
Kai Sun, Peibo Duan, Yongsheng Huang +4
Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (A…
MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks
Yongsheng Huang, Peibo Duan, Yujie Wu +7
Spiking Neural Networks (SNNs) currently face a critical bottleneck: while individual neurons exhibit dynamic biological properties, their macro-scopic architectures remain confine…
We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification
Zhipeng Liu, Peibo Duan, Xuan Tang +6
The World Wide Web thrives on intelligent services that rely on accurate time series classification, which has recently witnessed significant progress driven by advances in deep le…
CogniSNN: Enabling Neuron-Expandability, Pathway-Reusability, and Dynamic-Configurability with Random Graph Architectures in Spiking Neural Networks
Yongsheng Huang, Peibo Duan, Yujie Wu +5
Spiking neural networks (SNNs), regarded as the third generation of artificial neural networks, are expected to bridge the gap between artificial intelligence and computational neu…
Gated Fusion Enhanced Multi-Scale Hierarchical Graph Convolutional Network for Stock Movement Prediction
Xiaosha Xue, Peibo Duan, Zhipeng Liu +3
Accurately predicting stock market movements remains a formidable challenge due to the inherent volatility and complex interdependencies among stocks. Although multi-scale Graph Ne…
TimeFormer: Transformer with Attention Modulation Empowered by Temporal Characteristics for Time Series Forecasting
Zhipeng Liu, Peibo Duan, Xuan Tang +6
Although Transformers excel in natural language processing, their extension to time series forecasting remains challenging due to insufficient consideration of the differences betw…