Normalizing Flows

Normalizing Flows are a machine learning technique that transforms simple, known probability distributions into more complex ones by applying a series of smooth, invertible functions. Think of it as shaping a basic shape (like a ball) into a more intricate form (like a sculpture) without losing any information. This process allows the model to learn complex data patterns while maintaining the ability to easily calculate probabilities and generate new data points. Normalizing flows are useful in tasks like data generation, density estimation, and modeling intricate distributions in a mathematically sound and flexible way.