energy-based models

Energy-based models (EBMs) are a type of machine learning framework that learn to associate inputs with outputs by representing them in a way that assigns a numerical "energy" value. The goal is to minimize this energy for correct pairs while maximizing it for incorrect ones. Think of it like a system that learns to recognize patterns or features by creating a landscape of energies, where lower energy represents better matches. This approach can be used in tasks like image recognition, natural language processing, and more, helping machines understand and generate complex data effectively.