IEEE

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

Abstract: Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across ...
IEEE

Resolving large-scale control and optimization through network structure analysis and decomposition: A tutorial review

Abstract: Decomposition is a fundamental principle of resolving complexity by scale, which is utilized in a variety of decomposition-based algorithms for control and optimization. In this paper, we ...
GitHub

A beginner-friendly, hands-on tutorial for learning Large Language Model (LLM) training optimization techniques.

This project provides a minimal, easy-to-understand codebase for fine-tuning Large Language Models. Our core philosophy is to explain complex optimization techniques with the simplest possible code.
marktechpost

How to Align Large Language Models with Human Preferences Using Direct Preference Optimization, QLoRA, and Ultra-Feedback

In this tutorial, we implement an end-to-end Direct Preference Optimization workflow to align a large language model with human preferences without using a reward model. We combine TRL’s DPOTrainer ...
GitHub

Neural Pupil Engineering for off-axis Fourier Ptychography

This is the MATLAB code for the implementation of neural pupil engineering FPM (NePE-FPM), an optimization framework for FPM reconstruction for off-axis areas. NePE-FPM engineers the pupil function ...