Learning Convolutional Neural Networks from Ordered Features of Generic Data

Convolutional neural networks (CNN) have become very popular for computer vision, text, and sequence tasks. CNNs have the advantage of being able to learn local patterns through convolution filters. However, generic datasets do not have meaningful local data correlations, because their features are assumed to be independent of each other. In this paper, we propose an approach to reorder features of a generic dataset to create feature...