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Differentiable neural computer

A differentiable neural computer being trained to store and recall dense binary numbers. Performance of a reference task during training shown. Upper left: the input (red) and target (blue), as 5-bit words and a 1 bit interrupt signal. Upper right: the model's output.

In artificial intelligence, a differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (but not by definition) recurrent in its implementation. The model was published in 2016 by Alex Graves et al. of DeepMind.[1]

  1. ^ Cite error: The named reference DNCnature2016 was invoked but never defined (see the help page).

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