Title: Symmetry Structured Convolutional Neural Networks

Abstract: We will consider Convolutional Neural Networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions. Such networks arise in modeling pairwise relationships for example a sequential recommendation problem. We will introduce a CNN architecture that generates and preserves the symmetry structure in the network's convolutional layers. We will present parameterizations for the convolutional kernels that produce update rules to maintain symmetry throughout the training. Lastly, we will show that the symmetric structured networks produce improved results using fewer numbers of machine parameters.