We introduce the Redundant Information Neural Estimator (RINE), a method that allows efficient estimation for the component of information about a target variable that is common to a set of sources, known as the “redundant information.” We show that existing definitions of the redundant information can be recast in terms of an optimization over a family of functions. In contrast to previous information decompositions, which can only be evaluated for discrete variables over small alphabets, we show that optimizing over functions enables the approximation of the redundant information for high-dimensional and continuous predictors. We demonstrate this on high-dimensional image classification and motor-neuroscience tasks.

中文翻译：

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冗余信息神经估计

我们引入了冗余信息神经估计器 (RINE)，该方法可以有效估计有关一组源共有的目标变量的信息成分，称为“冗余信息”。我们表明，冗余信息的现有定义可以根据对一系列函数的优化来重新定义。与之前只能对小字母表上的离散变量进行评估的信息分解相比，我们表明，优化函数可以逼近高维和连续预测变量的冗余信息。我们在高维图像分类和运动神经科学任务中证明了这一点。