Architecture Evolution
Multi-Layer Perceptrons (MLPs) utilize fixed activation functions on nodes. Kolmogorov-Arnold Networks (KAN), proposed by researchers at MIT and Caltech, replace fixed activations with learnable univariate functions on edges. This design is based on the Kolmogorov-Arnold representation theorem.
Technical Advantages
- Symbolic Interpretability: KANs enable the discovery of underlying mathematical relationships within datasets through symbolic regression.
- Parameter Efficiency: Research indicates that KANs can achieve higher accuracy than standard MLPs with fewer parameters.
- Catastrophic Forgetting Mitigation: The local nature of spline-based activations reduces the interference typical of global weight updates in standard neural networks.
Operational Goal
The implementation of KANs addresses the requirement for Auditable AI. By representing activations as readable mathematical formulas, the architecture facilitates the verification of learned relationships in data-driven models.