polynomial-functors

What problem does it solve?

This Skill provides a framework for modeling compositional learning systems and dynamical systems using the Spivak-Niu polynomial functor theory, enabling robust composition of machine learning components.

Core Features & Use Cases

• Compositional Learning: Build complex learning systems by composing simpler learners (e.g., neural network layers) as polynomial functors.
• Dynamical Systems Modeling: Represent and simulate the evolution of systems over time using charts and polynomial structures.
• Use Case: Model a reinforcement learning agent as a series of composed polynomial functors, where each functor represents a different aspect of the agent's learning process or internal state dynamics.

Quick Start

Use the polynomial-functors skill to compose two neural network layers for sequential processing.