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In the ever-evolving landscape of artificial intelligence and machine learning, a new class of generative models has emerged, captivating researchers and practitioners alike: Diffusion Models. These powerful algorithms have taken the AI world by storm, producing results that push the boundaries of what we thought possible in computer-generated content. Diffusion Models represent a paradigm shift in how we approach generative tasks. Unlike their predecessors, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), Diffusion Models draw inspiration from thermodynamics and stochastic processes to create a unique approach to content generation. At their core, Diffusion Models work by gradually adding noise to data and then learning to reverse this process. In this blog post, we’ll embark on a journey to understand Diffusion Models from the ground up. We’ll explore the underlying principles, dive into the mathematics that makes them work, and even implement a simple version to see these fascinating models in action. Let’s begin our journey into the world of Diffusion Models!
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Sabri M., Maturo F., Verde R., Riffi J. and Yahyaouy A. E. Classification of ECG signals based on functional data analysis and machine learning techniques; IES 2022 Innovation & Society 5.0: Statistical and Economic Methodologies for Quality Assessment. Page: 618-623.
Sabri M., Verde R., Maturo F., Balzanella A., Tairi H., Yahyaouy A., and Riffi J. A new supervised classification technique based on the joint use of K-nearest neighbors and weighted K-means to discover new patterns in the data; SDS 2023 Statistics for Data Science and Artificial Intelligence.
Sabri M., Verde R., Balzanella A. Advancing credit card fraud detection with innovative class partitioning and feature selection technique; SDS 2024 Statistics for Data Science and Artificial Intelligence. Page: 618-623.
Sabri M., Yahyaouy A., Balzanella A., Verde R., Riffi J. and Tairi H. Functional Local Mean K-Nearest Neighbor: introducing a novel metric for improved algorithm performance; International Conference on Intelligent Systems and Computer Vision (ISCV 2024).
Sabri M., Verde R., Maturo F., Balzanella A., Tairi H., Yahyaouy A., and Riffi J. A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors; Journal of Classification (2024): 1-25.
Sabri M., Verde R. and Balzanella A. K‐Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy; Applied Stochastic Models in Business and Industry (2024).
Verde R., Sabri M., Balzanella A. A Classification method for functional data based on a refining strategy of the example data; 29èmes Rencontres de la Société Francophone de Classification (SFC).
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.