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Found 7 papers, 4 papers with code
Spiking Wavelet Transformer
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs).
AutoST: Training-free Neural Architecture Search for Spiking Transformers
To address these limitations, we introduce AutoST, a training-free NAS method for Spiking Transformers, to rapidly identify high-performance Spiking Transformer architectures.
Spiking Denoising Diffusion Probabilistic Models
In our paper, we put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new class of SNN-based generative models that achieve high sample quality.
AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density
DBSCAN has been widely used in density-based clustering algorithms.
Masked Spiking Transformer
The combination of Spiking Neural Networks (SNNs) and Transformers has attracted significant attention due to their potential for high energy efficiency and high-performance nature.
Video-Based Inpatient Fall Risk Assessment: A Case Study
Inpatient falls are a serious safety issue in hospitals and healthcare facilities.
