2 citations · 3 across the 6 of their papers we have counts for
7 papers
Spike-driven Vision-Language-Action Model
Shuai Wang, Malu Zhang, Mingquan Liu +6
Vision-language-action (VLA) models bridge multimodal understanding and robotic control, advancing the dominant paradigm for embodied intelligence. However, most existing models re…
Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes
Hanqing Liu, Liu Hanqing, Jianjun Cao +2
Deep neural networks exhibit periodic loss spikes during unregularized long-term training, a phenomenon known as the "Slingshot Mechanism." Existing work usually attributes this to…
TP-Spikformer: Token Pruned Spiking Transformer
Wenjie Wei, Xiaolong Zhou, Malu Zhang +8
Spiking neural networks (SNNs) offer an energy-efficient alternative to traditional neural networks due to their event-driven computing paradigm. However, recent advancements in sp…
Robust Spiking Neural Networks Against Adversarial Attacks
Shuai Wang, Malu Zhang, Yulin Jiang +7
Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing due to their bio-plausible and spike-driven characteristics. However, the…
Towards Accurate Binary Spiking Neural Networks: Learning with Adaptive Gradient Modulation Mechanism
Yu Liang, Wenjie Wei, Ammar Belatreche +5
Binary Spiking Neural Networks (BSNNs) inherit the eventdriven paradigm of SNNs, while also adopting the reduced storage burden of binarization techniques. These distinct advantage…
QP-SNN: Quantized and Pruned Spiking Neural Networks
Wenjie Wei, Malu Zhang, Zijian Zhou +6
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient pa…
Binary Event-Driven Spiking Transformer
Honglin Cao, Zijian Zhou, Wenjie Wei +6
Transformer-based Spiking Neural Networks (SNNs) introduce a novel event-driven self-attention paradigm that combines the high performance of Transformers with the energy efficienc…