Neural Turing Machines
Neural Turing Machines extend the capabilities of neural networks by coupling them to an external memory matrix, which they can selectively interact with. The combined system embodies a kind of 'differentiable computer' which can be trained with gradient descent. This talk describes how neural Turing machines can learn basic computational algorithms such associative recall from input and output examples only.
Alex Graves, PhD A world-renowned expert in Recurrent Neural Networks and Generative Models. Alex has done a BSc in Theoretical Physics at Edinburgh, Part III Maths at Cambridge, a PhD in AI at IDSIA. Followed by postdocs at TU-Munich and with Prof. Geoff Hinton at the University of Toronto. Most recently Alex has been spearheading our work on Neural Turing Machines.