NFT-K: NON-FUNGIBLE TANGENT KERNELS

Deep neural networks have become quintessential to numerous applications due to their strong empirical performance. Unfortunately, these networks are quite difficult to interpret and understand why they are working. One type of deep neural network is neural tangent kernel that is similar to a kernel machine that provides some aspect of interpretability. To further contribute interpretability with respect to classification and the layers, we develop a new network as a combination of multiple neural tangent kernels, one to model each layer of the deep neural network individually as opposed to past work which attempts to represent the entire network via a single neural tangent kernel. We demonstrate the interpretability of this model on two datasets, showing that the multiple kernels model elucidates the interplay between the layers and predictions.