energy-based model

An energy-based model is a type of machine learning approach that assigns a numerical value, called energy, to different configurations or states of data. The goal is to organize these states so that correct or likely ones have low energy, while unlikely or incorrect ones have high energy. The model learns to adjust these energies based on patterns seen in data, allowing it to distinguish or generate new data similar to what it has learned. Essentially, it creates an internal landscape where the most meaningful or accurate states are at the lowest points.