From noise to information: discriminative tasks based on randomized neural networks and generative tasks based on diffusion models

Abstract

In this thesis, I delve into the realm of noise and information, exploring the application and capabilities of randomized neural networks in discriminative tasks, as well as the utilization of diffusion models in generative tasks. I begin by investigating the inherent randomness in neural networks, and how this randomness can be harnessed to perform discriminative tasks with high accuracy. I then transition to the domain of generative tasks, where I employ diffusion models to generate high-quality data from noise. The primary innovations include: