In this paper, we introduce Variational Inference for Concept Embeddings (VICE), an approximate Bayesian method for learning object concept embeddings from human behavior in an odd-one-out triplet task. We use variational inference to obtain a sparse, non-negative solution with uncertainty estimates about each embedding value. We exploit these estimates to automatically select the dimensions that explain the data while yielding reproducible embeddings. We introduce a PAC learning bound for VICE that can be used to estimate generalization performance or determine a sufficient sample size for different experimental designs. VICE rivals or outperforms its predecessor, SPoSE, at predicting human behavior in a triplet task. VICE object representations are substantially more reproducible and consistent across different random initializations.

翻译：在本文中,我们引入了概念嵌入(VICE)的变量推断法,这是一种近似贝叶斯学用的方法,用于学习从人类行为中嵌入的物体概念,这种方法是一种奇数一出三重任务。我们使用变式推断法获得一种稀有的非负式解决办法,对每个嵌入值进行不确定性估计。我们利用这些估计法自动选择解释数据的维度,同时产生可复制的嵌入。我们引入了一种用于VICE的PAC学习定界,可用于估计一般化性能或确定不同实验设计的足够样本大小。VICE对立或超出其前身SPOSE在三重任务中预测人类行为的前身SPOSE。 VICE的物体表示方式在不同的随机初始化中具有很大的再复制性和一致性。