论文标题
恶习：各种可解释的概念嵌入
VICE: Variational Interpretable Concept Embeddings
论文作者
论文摘要
认知科学中的一个核心目标是开发用于对象概念的心理表示的数值模型。本文介绍了可解释的概念嵌入（VICE），这是一种近似的贝叶斯方法，用于将对象概念嵌入矢量空间中，并使用从人类中收集的数据中收集的三胞胎奇数任务中收集的数据。 VICE使用各种推理来获得对象概念的稀疏，非阴性表示，并对嵌入值进行不确定性估计。这些估计用于自动选择最能解释数据的尺寸。我们得出了用于VICE的PAC学习，该学习可用于估计概括性能或确定实验设计的足够样本量。副竞争对手或胜过其前身，在预测三胞胎奇数任务中的人类行为方面所构想。此外，VICE的对象表示在随机初始化中更可重复和一致，强调了使用VICE从人类行为中衍生出可解释的嵌入的独特优势。
A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task. VICE uses variational inference to obtain sparse, non-negative representations of object concepts with uncertainty estimates for the embedding values. These estimates are used to automatically select the dimensions that best explain the data. We derive a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for experimental design. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in the triplet odd-one-out task. Furthermore, VICE's object representations are more reproducible and consistent across random initializations, highlighting the unique advantage of using VICE for deriving interpretable embeddings from human behavior.
