Recursive Models

Recursive models are computational frameworks that solve problems by breaking them down into smaller, similar sub-problems. Instead of tackling the entire task at once, they use a process where the solution of a larger problem depends on solutions to its smaller versions. This repeated self-referential approach allows the model to handle complex data or sequences efficiently. For example, in natural language processing, recursive models analyze sentences by understanding smaller parts (like words or phrases) and building up to grasp the entire meaning. They are especially useful for structured, hierarchical data.