State-of-the-art neural network training methods depend on the gradient of the network
function. Therefore, they cannot be applied to networks whose activation functions do not …

We introduce a mathematically rigorous framework based on rough path theory to model
stochastic spiking neural networks (SSNNs) as stochastic differential equations with event …

The rising complexity and data generation in cyber-physical systems and the Internet of
Things require a shift towards an edge-to-cloud computing continuum ecosystem with …

In-memory computing (IMC) architectures mitigate the von Neumann bottleneck
encountered in traditional deep learning accelerators. Its energy efficiency can realize deep …

Weight initialization significantly impacts the convergence and performance of neural
networks. While traditional methods like Xavier and Kaiming initialization are widely used …