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[PDF][PDF] Toward robust spiking neural network against adversarial perturbation
As spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical
applications, the security concerns in SNNs attract more attention. Currently, researchers …
applications, the security concerns in SNNs attract more attention. Currently, researchers …
Threaten spiking neural networks through combining rate and temporal information
Spiking Neural Networks (SNNs) have received widespread attention in academic
communities due to their superior spatio-temporal processing capabilities and energy …
communities due to their superior spatio-temporal processing capabilities and energy …
Special session: Towards an agile design methodology for efficient, reliable, and secure ML systems
The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …
However, the current computing infrastructure is insufficient to support all real-world …
Enhancing adversarial robustness in SNNs with sparse gradients
Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient
operations and biologically inspired structures, offering potential advantages over Artificial …
operations and biologically inspired structures, offering potential advantages over Artificial …
Robust stable spiking neural networks
Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low
energy budget on neuromorphic hardware. However, they still face challenges in lacking …
energy budget on neuromorphic hardware. However, they still face challenges in lacking …