10 papers
SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks
Kai Sun, Peibo Duan, Zhanglu Yan +3
On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synapti…
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…
DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification
Zhipeng Liu, Peibo Duan, Binwu Wang +5
Real-world time series typically exhibit complex temporal variations, making the time series classification task notably challenging. Recent advancements have demonstrated the pote…
ILIF: Temporal Inhibitory Leaky Integrate-and-Fire Neuron for Overactivation in Spiking Neural Networks
Kai Sun, Peibo Duan, Levin Kuhlmann +2
The Spiking Neural Network (SNN) has drawn increasing attention for its energy-efficient, event-driven processing and biological plausibility. To train SNNs via backpropagation, su…
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer
Jinhui Yuan, Shan Lu, Peibo Duan +1
Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node…