2 citations · 2 across the 1 of their papers we have counts for
12 papers
Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks
Bing Han, Feifei Zhao, Wenxuan Pan +4
The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms…
TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers
Sicheng Shen, Mingyang Lv, Bing Han +4
In recent years, Spiking Neural Networks (SNNs) have achieved remarkable progress, with Spiking Transformers emerging as a promising architecture for energy-efficient sequence mode…
Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanism
Bing Han, Feifei Zhao, Yinqian Sun +2
Cognitive functions in current artificial intelligence networks are tied to the exponential increase in network scale, whereas the human brain can continuously learn hundreds of co…
CAFEs: Cable-driven Collaborative Floating End-Effectors for Agriculture Applications
Hung Hon Cheng, Josie Hughes
CAFEs (Collaborative Agricultural Floating End-effectors) is a new robot design and control approach to automating large-scale agricultural tasks. Based upon a cable driven robot a…
MTDP: A Modulated Transformer based Diffusion Policy Model
Qianhao Wang, Yinqian Sun, Enmeng Lu +2
Recent research on robot manipulation based on Behavior Cloning (BC) has made significant progress. By combining diffusion models with BC, diffusion policiy has been proposed, enab…
Brain-inspired Action Generation with Spiking Transformer Diffusion Policy Model
Qianhao Wang, Yinqian Sun, Enmeng Lu +2
Spiking Neural Networks (SNNs) has the ability to extract spatio-temporal features due to their spiking sequence. While previous research has primarily foucus on the classification…