conditional neural process

Conditional neural processes (CNPs) are a class of machine learning models designed to efficiently learn distributions over functions, combining the flexibility of neural networks with the data efficiency of Gaussian processes. They provide a framework for rapid adaptation to new tasks by conditioning on observed data.