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English(EN) Breaking the Simplification Bottleneck in Amortized Neural Symbolic Regression

SimpliPy引擎将神经符号回归加速100倍

研究人员开发了SimpliPy,一个新颖的基于规则的简化引擎,显著加快了符号回归过程。该引擎在现有计算机代数系统(如SymPy)上实现了100倍的速度提升,同时保持了可比的质量。SimpliPy引擎能够实现更高效的摊销神经符号回归训练和推理,从而能够扩展到更大的数据集并更好地利用计算资源。利用SimpliPy的新Flash-ANSR框架在基准测试中显示出比以前的摊销方法和直接优化方法更高的准确性和简洁性。 AI

影响 符号回归的这一进步可能导致更有效地发现科学数据的解析表达式。

排序理由 该集群包含一篇详细介绍符号回归新方法和框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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SimpliPy引擎将神经符号回归加速100倍

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该集群包含一篇详细介绍符号回归新方法和框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Paul Saegert, Ullrich K\"othe ·

    打破摊销神经符号回归中的简化瓶颈

    arXiv:2602.08885v5 Announce Type: replace-cross Abstract: Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises to be much more efficient than the predominant genetic programming SR methods, bu…