Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...

Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance. However, in new research I have used a phenomenon called “quantum tunnelling” to ...

The fusion of artificial neural networks (NNs) with photonics has generated significant implications across various scientific fields and industries. A new tutorial review published in the journal ...