论文标题
非确定性推理的表格特征的随机扰动
Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge
论文作者
论文摘要
众所周知，将高斯噪声注入训练特征是正则化的。本文认为将噪声注射到数字或分类表格特征中，这将转化为推理，这将推断转化为非确定性结果，并可能与公平考虑，对抗性示例保护或其他受益于非确定性主义的用例有关。我们提供用于表格预处理的汽车库作为该实践的资源，其中包括将随机抽样或熵播种的选项与量子电路的支持，代表一种将量子算法传播到经典学习的新方法。
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.
