Conditional Neural Process

Conditional Neural Processes (CNPs) are a class of probabilistic models using neural networks to learn how to predict functions from data, excelling in few-shot learning scenarios. Research focuses on improving CNP efficiency and expressiveness through architectural innovations like convolutional and spectral convolutional variants, as well as incorporating dependencies between predictions using autoregressive methods or latent variable models. These advancements enhance CNP applicability in diverse fields, including autonomous vehicles, robotics, and scientific modeling, by enabling more accurate and efficient predictions from limited data.