Juniper is an AI/ML research platform for investigating dynamic neural network architectures and novel learning paradigms. The project emphasizes ground-up implementations from primary literature, enabling a more transparent exploration of fundamental algorithms.
juniper_cascor: Cascade Correlation Neural Network
- Reference implementation from foundational research (Fahlman & Lebiere, 1990)
- Designed for flexibility, modularity, and scalability
- Enables investigation of constructive learning algorithms
juniper_canopy: Interactive Research Interface
- Research-driven monitoring and visualization environment
- Delivers novel observations through real-time network introspection
- Transforms metrics into insights, accelerating experimental iteration
Juniper prioritizes transparency over convenience and understanding over abstraction. By implementing algorithms from first principles, the platform provides researchers with increased visibility into network behavior, enabling a more rigorous and more controlled investigation of learning dynamics and architectural innovations.