Kolmogorov-Arnold Networks (KANs) as a drop-in replacement for MLPs
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Kolmogorov-Arnold Networks (KANs) offer an alternative to Multi-Layer Perceptrons (MLPs) by placing activation functions on edges rather than nodes, grounded in the Kolmogorov-Arnold representation theorem. This architectural shift aims to improve model accuracy and interpretability, particularly for scientific computing tasks. The project provides a Python library for implementing and experimenting with KANs.
How It Works
KANs leverage the Kolmogorov-Arnold representation theorem, which states that any continuous function of several variables can be represented as a finite sum of continuous functions of one variable. Unlike MLPs, where activation functions are applied at nodes, KANs apply learnable activation functions to the edges connecting neurons. This allows KANs to potentially represent complex functions more efficiently and with greater interpretability than traditional MLPs.
Quick Start & Requirements
pip install pykan
or pip install -e .
after cloning the repository.Highlighted Details
model.plot()
for visualization and model.prune()
for interpretability-focused refinement.Maintenance & Community
The project is maintained by KindXiaoming. The author notes that the code was developed with small-scale math and physics examples in mind and may lack efficiency or reusability optimizations. Further development for efficiency is not a primary focus, with external projects like efficientkan
and fouierkan
suggested.
Licensing & Compatibility
The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial or closed-source use.
Limitations & Caveats
The author acknowledges that KANs may not be a simple plug-in for general machine learning tasks and require significant hyperparameter tuning. Efficiency and reusability are noted as areas for improvement, and the current implementation is not optimized for large-scale ML. The applicability to LLMs is uncertain due to differing definitions of accuracy and interpretability.
6 months ago
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