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: 1. Part I: Randomized Neural Networks for Discriminative Tasks - The introduction of an unsupervised learning approach and self-distillation for Randomised neural networks, which has improved the efficiency of utilizing limited data resources and has also enhanced the overall capabilities of the model. - The development of an ensemble deep RVFL network for regression tasks, incorporating techniques such as boosting factors, skip connections, and an ensemble scheme for improved predictive power. - More structures for Randomised Neural Networks, including Automated Layer-wise Solution, Adaptive Ensemble, Deep Reservoir variants and Noise Elimination. Our broader objective is to extend the utility of RVFL networks across various domains and applications. 2. Part II: Diffusion Models for Generative Tasks - The design of a Vector Quantized Discrete Diffusion Model (VQ-DDM) for efficient and high-fidelity image generation, which employs a…
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