1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
A2SG:Adaptive and Asymmetric Surrogate Gradients for Training Deep Spiking Neural Networks
Yechan Kang, Yongjin Kweon, Mingyeong Seo +8
Training deep spiking neural networks (SNNs) remains challenging due to sharp loss landscapes and temporal inconsistency caused by surrogate gradients. To address these challenges,…
A More Accurate Approximation of Activation Function with Few Spikes Neurons
Dayena Jeong, Jaewoo Park, Jeonghee Jo +5
Recent deep neural networks (DNNs), such as diffusion models [1], have faced high computational demands. Thus, spiking neural networks (SNNs) have attracted lots of attention as en…
Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information
Seongsik Park, Jeonghee Jo, Jongkil Park +9
Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations. To tra…
Markov chain Hebbian learning algorithm with ternary synaptic units
Guhyun Kim, Vladimir Kornijcuk, Dohun Kim +6
In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due i…