论文标题
AI Feynman 2.0：帕累托 - 最佳符号回归利用图模块化
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
论文作者
论文摘要
我们提出了一种改进的符号回归方法，该方法旨在将数据拟合到帕累托最佳的公式中，从而使给定复杂性具有最佳准确性。通常，它通常是对噪声和不良数据的较大数量级，并发现许多使以前方法陷入困境的公式，从而改善了先前的最先进。我们开发了一种从神经网络拟合的梯度属性中发现广义对称性的方法（在公式的计算图中的任意模块化）。我们使用归一化的流程将我们的符号回归方法推广到只有样本的概率分布，并采用统计假设测试来加速强大的蛮力搜索。
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It improves on the previous state-of-the-art by typically being orders of magnitude more robust toward noise and bad data, and also by discovering many formulas that stumped previous methods. We develop a method for discovering generalized symmetries (arbitrary modularity in the computational graph of a formula) from gradient properties of a neural network fit. We use normalizing flows to generalize our symbolic regression method to probability distributions from which we only have samples, and employ statistical hypothesis testing to accelerate robust brute-force search.
