Architecture-Induced Recoverability Bias in Differentiable Symbolic Regression 事件

Architecture-Induced Recoverability Bias in Differentiable Symbolic Regression arXiv:2604.23256v2 Announce Type: replace-cross Abstract: Symbolic regression aims to recover closed-form expressions from numerical data, but in differentiable symbolic regression the recovered expression depends not only on the grammar but also on the fixed architecture through which variables are routed during training. This is relevant to signal-processing settings in which closed-form models and interpretable no