Over the last decades, with neural networks and deep learning, several powerful architectures have been proposed, including e.g. convolutional neural networks (CNN), stacked autoencoders, deep Boltzmann machines (DBM), deep generative models and generative adversarial networks (GAN). On the other hand, with support vector machines (SVM) and kernel machines, solid foundations in learning theory and optimization have been achieved. Could one combine the best of both worlds?
Johan Suykens | KU Leuven, ESAT-Stadius and Leuven.AI Institute