研究人员开发了一种名为 Diversified Residual Symbolic Regression (DRSR) 的新方法，以应对符号回归任务中的异常值挑战。当数据包含异常观测值时，传统方法难以识别潜在模式。DRSR 旨在提供多个候选数学表达式，这些表达式能很好地解释数据，但在处理残差方面有所不同，从而允许用户根据其领域知识选择最合适的模型。 AI
影响 通过更好地处理现实世界数据的复杂性，引入了一种新颖的方法来提高符号回归模型的可解释性和准确性。
排序理由 该集群包含一篇详细介绍符号回归新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
在 arXiv cs.NE (Neural & Evolutionary) 阅读 →
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arXiv:2605.27245v1 Announce Type: new Abstract: Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directly to expressions in a single pass, but this amortize…
Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directly to expressions in a single pass, but this amortized inference leaves a residual amortization gap b…
Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directly to expressions in a single pass, but this amortized inference leaves a residual amortization gap b…
Symbolic regression (SR) aims to discover explicit mathematical expressions that explain observed data and is widely used in domains where interpretability is essential. Because interpretability requires expressions to reflect meaningful regularities, SR is sensitive to observati…