Paper Summary #2: Convolutional Neural Network on Graphs with Fast Localized Spectral Filtering (NIPS 2016)
This post summarizes the NIPS 2016 paper by Defferrard et al on " Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering ". Convolutional Neural Networks have accomplished many breakthroughs, ranging from a classification of a million images of ImageNet to very complex tasks like object tracking in surveillance videos. These advancements are not restricted to image data. The CNNs (and, in general, deep learning concepts) have been able to achieve state-of-the-art results even on text and speech applications. CNNs are proved to be very powerful tool in solving many problems from images, text and speech domain. If that is the case then the question that we want to ask here is, can we use CNNs to solve problems on graphs as well. If we take a closer look at the data domain that we were dealing with, we realize that this data has specific structure, e.g. images are nothing but the 2-D grid of pixels, the text is a stream of words and can be t