7 citations · 28 across the 43 of their papers we have counts for
8 papers · 1 filter
Pursing the Sparse Limitation of Spiking Deep Learning Structures
Hao Cheng, Jiahang Cao, Erjia Xiao +7
Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased attention for their superior computation and energy efficiency over traditional artificial…
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
Ziqing Wang, Yuetong Fang, Jiahang Cao +1
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Despite this, bridging the performance gap…
Fully Spiking Neural Network for Legged Robots
Xiaoyang Jiang, Qiang Zhang, Jingkai Sun +3
Recent advancements in legged robots using deep reinforcement learning have led to significant progress. Quadruped robots can perform complex tasks in challenging environments, whi…
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias
Hao Cheng, Jinhao Duan, Hui Li +6
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP). Compared with…
Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Network
Hao Cheng, Jiahang Cao, Erjia Xiao +2
Deploying energy-efficient deep learning algorithms on computational-limited devices, such as robots, is still a pressing issue for real-world applications. Spiking Neural Networks…
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
Jiahang Cao, Xu Zheng, Yuanhuiyi Lyu +3
The ability to detect objects in all lighting (i.e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving.Traditional RGB-base…
Spiking Denoising Diffusion Probabilistic Models
Jiahang Cao, Ziqing Wang, Hanzhong Guo +3
Spiking neural networks (SNNs) have ultra-low energy consumption and high biological plausibility due to their binary and bio-driven nature compared with artificial neural networks…
Self-Motivated Multi-Agent Exploration
Shaowei Zhang, Jiahan Cao, Lei Yuan +2
In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve a balance between self-exploration and team collaboration. However, agents can hardl…