NSFn
NSFn, short for Neural-Symbolic Function Network, is a framework designed to learn and manipulate mathematical functions by integrating neural networks with symbolic computation. The approach seeks to combine the representational power of neural models with the interpretability of symbolic expressions, enabling models that can approximate complex functions while yielding human-readable formulas.
Architecture: NSFn typically comprises three parts: a neural learner that processes raw input data and captures
Training: Models are trained with a differentiable proxy that allows gradient-based optimization across both neural and
Applications: NSFn is applied to symbolic regression, scientific discovery, and any domain requiring interpretable function models,
Evaluation: Typical benchmarks assess not only predictive accuracy but also the simplicity and plausibility of discovered
Limitations: As with other neuro-symbolic approaches, NSFn can be computationally intensive and sensitive to initialization and
See also: neuro-symbolic AI, symbolic regression, differentiable programming.