Memory Networks

Memory Networks are models equipped with a storage component where information can generally be written and successively retrieved for any purpose. Simple forms of memory networks like the popular recurrent neural networks (RNN), LSTMs or GRUs, have limited storage capabilities and for specific tasks. In contrast, recent works, starting from Memory Augmented Neural Networks, overcome storage and computational limitations with the addition of a controller network with an external element-wise addressable memory. This tutorial aims at providing an overview of such memory-based techniques and their applications in multimedia. It will cover an explanation of the basic concepts behind recurrent neural networks and will then delve into the advanced details of memory augmented neural networks, their structure and how such models can be trained. We target a broad audience, from beginners to experienced researchers, offering an in-depth introduction to an important crop of literature which is starting to gain interest in the multimedia, computer vision and natural language processing communities.