表特征的随机扰动在非确定性自动推理中的应用
Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge
摘要：
众所周知，将高斯噪声注入训练特征具有正则化特性。本文考虑将噪声注入到数字或分类表格特征中传递给推理，这将推理转换为非确定性结果，并且可能与公平性考虑，对抗性示例保护或受益于非确定性的其他用例相关。我们提供了用于表格预处理的Automunge库作为实践的资源，其中包括在量子电路的支持下集成随机采样或熵播种的选项，代表了将量子算法引入经典学习的新方法。
英文摘要：
Injecting gaussian noise into training features is well known to have regularization properties. This paper considers noise injections to numeric or categoric tabular features as passed to inference, which translates inference to a non-deterministic outcome and may have relevance to fairness considerations, adversarial example protection, or other use cases benefiting from non-determinism. We offer the Automunge library for tabular preprocessing as a resource for the practice, which includes options to integrate random sampling or entropy seeding with the support of quantum circuits, representing a new way to channel quantum algorithms into classical learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
