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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增强方程发现

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，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学习噪声鲁棒的函数逼近，然后应用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RMSE、噪声鲁棒性和泛化能力方面优于现有方法，为科学机器学习提供了一种可扩展且易于理解的方法。 AI

影响 通过结合神经网络逼近和方程发现，增强了从数据中获得的解释性和科学理解。

排序理由 该集群包含一篇研究论文，详细介绍了使用深度学习进行符号回归的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的神经符号回归框架增强方程发现

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该集群包含一篇研究论文，详细介绍了使用深度学习进行符号回归的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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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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