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English(EN) Neural Symbollic Regression Using Deep Learning and Sparse Modelling

新的神经符号回归框架结合了深度学习与稀疏建模

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新的神经符号回归（NSR）框架，该框架结合了神经网络和稀疏建模技术，以从数据中发现简洁的数学表达式。该方法首先使用神经网络在非线性特征空间中学习鲁棒的函数逼近，然后使用LASSO提取稀疏、可解释的方程。在Nguyen基准套件上的实验表明，NSR在均方根误差、噪声鲁棒性和泛化能力方面优于遗传编程和SINDy等传统方法。 AI

影响 该框架可以增强机器学习模型在科学发现中的可解释性。

排序理由 该集群描述了一篇详细介绍新颖符号回归框架的新研究论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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新的神经符号回归框架结合了深度学习与稀疏建模

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该集群描述了一篇详细介绍新颖符号回归框架的新研究论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ravi Kumar U, Sumitra S ·

    基于深度学习和稀疏建模的神经符号回归

    arXiv:2609.01102v1 Announce Type: new Abstract: Symbolic Regression (SR) seeks to find succinct mathematical expressions that represent the fundamental relationships within data, providing interpretability and scientific understanding that exceeds that of black-box models. Nevert…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Sumitra S ·

    基于深度学习和稀疏建模的神经符号回归

    Symbolic Regression (SR) seeks to find succinct mathematical expressions that represent the fundamental relationships within data, providing interpretability and scientific understanding that exceeds that of black-box models. Nevertheless, traditional methods like Genetic Program…

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