52 citations · 86 across the 14 of their papers we have counts for
14 papers
ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks
Yufei Guo, Yuhan Zhang, Zhou Jie +5
The Spiking Neural Network (SNN), a biologically inspired neural network infrastructure, has garnered significant attention recently. SNNs utilize binary spike activations for effi…
Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformer
Yufei Guo, Xiaode Liu, Yuanpei Chen +3
Transformers have demonstrated outstanding performance across a wide range of tasks, owing to their self-attention mechanism, but they are highly energy-consuming. Spiking Neural N…
Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking
Xin Tong, Shi Peng, Baojie Tian +3
Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confi…
Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks
Yufei Guo, Yuanpei Chen, Zecheng Hao +5
The Spiking Neural Network (SNN) is a biologically inspired neural network infrastructure that has recently garnered significant attention. It utilizes binary spike activations to…
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
Yufei Guo, Yuanpei Chen, Xiaode Liu +4
The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to…
Spiking PointNet: Spiking Neural Networks for Point Clouds
Dayong Ren, Zhe Ma, Yuanpei Chen +4
Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency, have drawn much research attention on 2D visual recognition and shown gradually increasing application…
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks
Yufei Guo, Yuanpei Chen, Liwen Zhang +5
The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing pa…
Membrane Potential Batch Normalization for Spiking Neural Networks
Yufei Guo, Yuhan Zhang, Yuanpei Chen +5
As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking neural networks (SNNs) have gained more and more interest recently. To train the deep mo…
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks
Yufei Guo, Xiaode Liu, Yuanpei Chen +5
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the r…
NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN
Yufei Guo, Yuanpei Chen, Zhe Ma
Recently, the neuromorphic vision sensor has received more and more interest. However, the neuromorphic data consists of asynchronous event spikes, which makes it difficult to cons…
Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization
Yufei Guo, Weihang Peng, Yuanpei Chen +4
Emerged as a biology-inspired method, Spiking Neural Networks (SNNs) mimic the spiking nature of brain neurons and have received lots of research attention. SNNs deal with binary s…
Direct Learning-Based Deep Spiking Neural Networks: A Review
Yufei Guo, Xuhui Huang, Zhe Ma
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and even…
Real Spike: Learning Real-valued Spikes for Spiking Neural Networks
Yufei Guo, Liwen Zhang, Yuanpei Chen +5
Brain-inspired spiking neural networks (SNNs) have recently drawn more and more attention due to their event-driven and energy-efficient characteristics. The integration of storage…
UNMAS: Multi-Agent Reinforcement Learning for Unshaped Cooperative Scenarios
Jiajun Chai, Weifan Li, Yuanheng Zhu +4
Multi-agent reinforcement learning methods such as VDN, QMIX, and QTRAN that adopt centralized training with decentralized execution (CTDE) framework have shown promising results i…