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kan-rs: Kolmogorov-Arnold Networks 🦀

kan-rs is a 🦀 implementation of Kolmogorov-Arnold Networks (KANs), a novel neural network architecture inspired by the Kolmogorov-Arnold representation theorem. KANs offer a unique approach to function approximation by combining continuous piecewise polynomial splines with symbolic functions. Specifically . It is a port of py-kan.

Key Innovations

  • Polynomial Spline Activation Functions: Unlike traditional MLPs that rely on fixed activation functions, KANs utilize continuous piecewise polynomial splines as activation functions. These splines provide a more flexible and expressive representation of complex functions.

  • Symbolic Representation: KANs introduce a symbolic representation layer alongside the numerical spline-based layer. This symbolic layer allows for interpretable and analytically tractable expressions of the learned function.

  • Function Approximation: KANs leverage the Kolmogorov-Arnold representation theorem to approximate any continuous multivariate function as a composition of univariate functions and the binary operation of addition. This theoretical foundation enables KANs to effectively learn and represent a wide range of functions.

Differences between py-KAN and kan-rs

  • Sympy: I have employed a rough hack to emulate Sympy's Computer Algebra System (CAS). I am still evaluating better ways to implement this.

Limitations

  • Limited Functionality: kan-rs provides a basic implementation of KANs and may not include all the features and extensions available in py-KAN. Additional functionality and customization options could be added to enhance the usability and flexibility of kan-rs.

  • Experimental Nature: KANs are a relatively new architecture, and their effectiveness and limitations are still being explored. While kan-rs aims to provide a faithful implementation, further research and empirical evaluation are necessary to fully understand the capabilities and limitations of KANs in various domains.

Educational Purpose

kan-rs is developed for educational purposes, serving as a learning resource for those interested in understanding and implementing KANs in 🦀. It provides a starting point for exploring the concepts and techniques behind KANs and encourages experimentation and further development.

However, it is important to note that kan-rs is not production-ready (and probably never will be) and should not be used in critical or real-world applications without thorough testing, validation, and additional development efforts. The implementation may have limitations, bugs, or performance issues that need to be addressed before considering it for production use.

Getting Started

To get started with kan-rs, refer to the documentation and examples provided in the repository. The code is organized into modules, and the main entry point is located in the src/main.rs file.

Contributing

Contributions to kan-rs are welcome! If you encounter any issues, have suggestions for improvements, or would like to add new features, please feel free to open an issue or submit a pull request. However, keep in mind that this project is primarily for educational purposes, and the focus is on learning and understanding the concepts behind KANs.

License

kan-rs is open-source and released under the MIT License. You are free to use, modify, and distribute the code for educational and non-commercial purposes. However, please note that the authors and contributors of kan-rs are not liable for any damages or consequences arising from the use of this software.

Acknowledgments

kan-rs is inspired by the original py-KAN implementation and the research on Kolmogorov-Arnold Representation Networks. We would like to acknowledge the authors and contributors of py-KAN for their valuable work and insights.

If you have any questions or need further assistance, please feel free to reach out. Happy learning and experimentation with kan-rs!

TODOs