Row 5565

Row ID: 5565 | Dataset Entry | Axioma AXP Content Repository

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**Paper**: [https://arxiv.org/abs/2404.19756](https://arxiv.org/abs/2404.19756)

**Code**: [https://github.com/KindXiaoming/pykan](https://github.com/KindXiaoming/pykan)

**Quick intro**: [https://kindxiaoming.github.io/pykan/intro.html](https://kindxiaoming.github.io/pykan/intro.html)

**Documentation**: [https://kindxiaoming.github.io/pykan/](https://kindxiaoming.github.io/pykan/)

**Abstract**:

>Inspired by the Kolmogorov-Arnold representation theorem, we propose **Kolmogorov-Arnold Networks** (**KANs**) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have *fixed* activation functions on *nodes* ("neurons"), KANs have *learnable* activation functions on *edges* ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.

https://preview.redd.it/r7vjmp31juxc1.png?width=2326&format=png&auto=webp&s=a2c722cf733510194659b9aaec24269a7f9e5d47

FieldValue
text **Paper**: [https://arxiv.org/abs/2404.19756](https://arxiv.org/abs/2404.19756) **Code**: [https://github.com/KindXiaoming/pykan](https://github.com/KindXiaoming/pykan) **Quick intro**: [https://kindxiaoming.github.io/pykan/intro.html](https://kindxiaoming.github.io/pykan/intro.html) **Documentation**: [https://kindxiaoming.github.io/pykan/](https://kindxiaoming.github.io/pykan/) **Abstract**: >Inspired by the Kolmogorov-Arnold representation theorem, we propose **Kolmogorov-Arnold Networks…
label r/machinelearning
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communityName r/MachineLearning
datetime 2024-05-01
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Raw Record

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  "text": "**Paper**: [https://arxiv.org/abs/2404.19756](https://arxiv.org/abs/2404.19756)\n\n**Code**: [https://github.com/KindXiaoming/pykan](https://github.com/KindXiaoming/pykan)\n\n**Quick intro**: [https://kindxiaoming.github.io/pykan/intro.html](https://kindxiaoming.github.io/pykan/intro.html)\n\n**Documentation**: [https://kindxiaoming.github.io/pykan/](https://kindxiaoming.github.io/pykan/)\n\n**Abstract**:\n\n>Inspired by the Kolmogorov-Arnold representation theorem, we propose **Kolmogorov-Arnold Networks** (**KANs**) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have *fixed* activation functions on *nodes* (\"neurons\"), KANs have *learnable* activation functions on *edges* (\"weights\"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.\n\nhttps://preview.redd.it/r7vjmp31juxc1.png?width=2326&format=png&auto=webp&s=a2c722cf733510194659b9aaec24269a7f9e5d47",
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