Exploring higher-order neural network node interactions with total correlation

All of these methods require either an input of and the human brain the variables interact interest or the class labels and are thus supervised. in complex ways. Yet accurately characterizing higher-order variable interactions (HOIs) is a difficult In response to the challenges posed by understanding neural problem that is further exacerbated when the networks and analyzing higher-order variable interactions HOIs change across the data. To solve this problem (HOIs), we present Local CorEx, a novel post hoc method we propose a new method called Local Correlation suitable for exploring model weights, nodes, subnetworks, Explanation (CorEx) to capture HOIs at a and latent representations in an unsupervised manner. Here local scale by first clustering data points based on we focus our attention on analyzing groups of hidden nodes their proximity on the data manifold. We then use and latent representations. To the best of our knowledge, our a multivariate version of the mutual information work marks the first post hoc method to do so in an unsupervised called the total correlation, to construct a latent manner and includes the option to easily incorporate factor representation of the data within each cluster label information. Additionally, our approach extends to to learn the local HOIs. We use Local CorEx analyzing HOIs within the data.