29 citations · 53 across the 6 of their papers we have counts for
7 papers
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models
Yuqiu Deng, Muyun Jiang, Zongpeng Zhang +8
EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pret…
Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training SNNs in a more biol…
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually lear…
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised…
Online Training Through Time for Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with…
Global Convergence of Over-parameterized Deep Equilibrium Models
Zenan Ling, Xingyu Xie, Qiuhao Wang +2
A deep equilibrium model (DEQ) is implicitly defined through an equilibrium point of an infinite-depth weight-tied model with an input-injection. Instead of infinite computations,…
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. However, the supervised training of SNNs remains a ha…