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English(EN) HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks

HYDRA架构通过双曲几何增强Kolmogorov-Arnold网络

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了HYDRA，一种通过结合双曲几何来扩展Kolmogorov-Arnold网络（KANs）的新架构。这种方法旨在减少KANs中的参数冗余，这会限制它们的扩展性和效率。HYDRA将输入映射到双曲潜在空间，并使用低秩原型块来共享函数变换，从而在保持各种基准测试中具有竞争力的预测性能的同时，提高了参数效率和可解释性。 AI

影响 引入了一种参数更有效、可解释性更强的神经网络架构，有望提高复杂函数逼近任务的可扩展性。

排序理由 该集群包含一篇详细介绍新神经网络架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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HYDRA架构通过双曲几何增强Kolmogorov-Arnold网络

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhao Su, Yuxin Xia, Haoran Li, Jun Shen, Qi Zhu, Qingguo Zhou, Binbin Yong ·

    HYDRA: 用于 Kolmogorov-Arnold 网络的高双曲动态表示架构

    arXiv:2608.12194v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) enhance nonlinear function approximation by replacing scalar weights with learnable univariate functions. However, assigning an independent function to every connection results in substantial para…

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