Recurrent Neural Networks and Other Machines that Learn Algorithms

Both questions found natural answers in the field of Recurrent Neural Networks (RNNs), which are brain-inspired general purpose computers that can learn parallel-sequential programs or algorithms encoded as weight matrices. Since then, a lot has happened. Some of the most successful applications in machine learning (including deep learning) are now driven by RNNs such as Long Short-Term Memory, e.g., speech recognition, video recognition, natural language processing, image captioning, time series prediction, etc. Reinforcement-learning and evolutionary RNNs are solving complex control tasks from raw video input. Many RNN-based methods learn sequential attention strategies. At this symposium, we will review the latest developments in all of these fields, and focus not only on RNNs, but also on learning machines in which RNNs interact with external memory such as neural Turing machines, memory networks, and related memory architectures such as fast weight networks and neural stack machines.