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

Intelligent Systems course project (MSc in Computer Engineering @ Unversity of Pisa). Design and development of a MLP, RBF networks and Fuzzy System to estimate person's affective state. Design, ...

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 ...

The 2024 Nobel Prize in Physics has been awarded to scientists John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural ...

Principal Research Fellow at AI and Cyber Futures Institute, Charles Sturt University Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance.

Abstract: An analysis was made of physics-informed neural networks used to solve partial differential equations. The prospects for the implementation of physics-informed neural networks in the MATLAB ...

"The tutorial presented will again tackle the MNIST digit classification problem. You can find more information from the previous [MNIST introduction](introduction_to_mnist.ipynb) tutorial. This time, ...