KindXiaoming/pykan

Features

• Edge-Based Activations - Replaces fixed node activations with learnable spline functions located on the network edges.
• Kolmogorov-Arnold Networks - Implements the Kolmogorov-Arnold Network architecture using learnable functions on edges.
• Sparsity Regularization - Employs specific solvers and penalty functions during training to encourage sparse and interpretable weight distributions.
• Model Interpretability Frameworks - Provides a toolkit for pruning and sparsification to derive transparent rules from complex models.
• Model Sparsification - Uses regularization-driven sparsification to force unimportant connections to zero for better interpretability.
• Neural Network Interpretability - Builds models where internal logic is converted into human-readable mathematical formulas for transparency.
• Spline-Based Approximations - Approximates complex non-linear mappings using learnable piecewise polynomial segments.
• Symbolic Library Matching - Converts learned numerical functions into mathematical formulas by matching them against predefined symbolic expressions.
• Symbolic Regression - Converts trained network functions into symbolic expressions to derive exact mathematical rules from data.
• Symbolic Regression Tools - Ships tools for converting trained neural network functions into human-readable mathematical formulas.
• Symbolic Formula Conversions - Converts learned spline activations into mathematical symbolic expressions using automated library matching.
• Symbolic Rule Extractions - Extracts human-readable mathematical rules from trained network functions to improve interpretability.
• Additive-Compositional Topologies - Implements an additive-compositional topology to represent high-dimensional multivariate functions.
• Model Pruning - Removes unimportant edges from the model to reduce complexity and increase interpretability.
• Neural Network Topology Visualizations - Provides utilities to visualize the model structure and inspect learnable activation functions throughout the training process.
• Spline-Based Learning Libraries - Provides a machine learning implementation using learnable spline functions to improve accuracy and transparency.
• Scene Hierarchy Pruning - Provides automated pruning of redundant nodes and edges to simplify the model graph.
• Scientific Machine Learning - Uses symbolic extraction and regularization to discover physical laws hidden within dataset patterns.

Star history