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Differentiable Neural Computers (Neural Networks with external memory) - waldo - 04-12-2017
Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning,
but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to
the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer
(DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the
random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent
and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained
with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate
reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest
path between specified points and inferring the missing links in randomly generated graphs, and then generalize these
tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC
can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together,
our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural
networks without external read–write memory.
http://www.nature.com/articles/nature20101.epdf?author_access_token=ImTXBI8aWbYxYQ51Plys8NRgN0jAjWel9jnR3ZoTv0MggmpDmwljGswxVdeocYSurJ3hxupzWuRNeGvvXnoO8o4jTJcnAyhGuZzXJ1GEaD-Z7E6X_a9R-xqJ9TfJWBqz