让表格回归模型既可解释又高效，用可微逻辑实现
Learning Interpretable Differentiable Logic Networks for Tabular Regression
- 将离散逻辑转为可微形式，支持连续目标输出
- 单阶段训练，15个基准上精度媲美甚至超过主流模型
- 适合需要透明推理和低计算开销的场景
神经网络在多个领域表现优异，但决策过程不透明且在资源受限环境下推理成本高。我们此前提出的可微逻辑网络（DLNs）通过将离散逻辑松弛为可微形式，实现了基于二值逻辑运算的网络梯度学习，兼具可解释性与低推理开销。本文将该框架扩展至监督式表格回归任务：重新设计输出层以支持连续目标，并将原两阶段训练统一为单一可微阶段。我们在15个公开回归基准上评估模型，结果表明，回归型DLNs在保持可解释性和快速推理的同时，精度达到或超越经典回归基线及现代神经网络，证明其在模型透明性与计算效率要求高的场景下是一种可行且高效的替代方案。
原文摘要 · Abstract (English)
Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in resource-constrained environments. We recently proposed Differentiable Logic Networks (DLNs) to address these issues for tabular classification based on relaxing discrete logic into a differentiable form, thereby enabling gradient-based learning of networks built from binary logic operations. DLNs offer interpretable reasoning and substantially lower inference cost. We extend the DLN framework to supervised tabular regression. Specifically, we redesign the final output layer to support continuous targets and unify the original two-phase training procedure into a single differentiable stage. We evaluate the resulting model on 15 public regression benchmarks, comparing it with modern neural networks and classical regression baselines. Regression DLNs match or exceed baseline accuracy while preserving interpretability and fast inference. Our results show that DLNs are a viable, cost-effective alternative for regression tasks, especially where model transparency and computational efficiency are important.
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