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Differentiable Neural Computers (Neural Networks with external memory) |
Enviado por: waldo - 04-12-2017, 02:52 AM - Foro: Neural Networks
- Sin respuestas
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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 random...
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