Automatic differentiation is a popular technique for computing derivatives of computer programs. While automatic differentiation has been successfully used in countless engineering, science, and machine learning applications, it can sometimes nevertheless produce surprising results. In this paper, we categorize problematic usages of automatic differentiation, and illustrate each category with examples such as chaos, time‐averages, discretizations, fixed‐point loops, lookup tables, linear solvers, and probabilistic programs, in the hope that readers may more easily avoid or detect such pitfalls. We also review debugging techniques and their effectiveness in these situations.This article is categorized under: Technologies > Machine Learning

中文翻译：

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自动微分陷阱的分类


自动微分是计算计算机程序导数的流行技术。尽管自动微分已成功应用于无数工程、科学和机器学习应用中，但它有时仍会产生令人惊讶的结果。在本文中，我们对自动微分的有问题的用法进行了分类，并用混沌、时间平均、离散化、定点循环、查找表、线性求解器和概率程序等示例来说明每个类别，希望读者能更多地了解自动微分的用法。轻松避免或发现此类陷阱。我们还回顾了调试技术及其在这些情况下的有效性。本文分类如下：技术 > 机器学习