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Differentiable neural computer

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.

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Differentiable neural computer

Nodes28
Edges27
Triples6
Avg. degree1.93
Density0.071429
Components1

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Differentiable neural computer

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related to External links · 2
Differentiable neural computer → Deeply, Differentiable Neural Network Thinks

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Important terminology

dnc memory neural network architecture tasks dncs differentiable using trained model published problem better turing time typically recurrent applications traditional

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
graphs sequentiallyinstance ofThis attention span allows the user to feed complex data structures0.80text
and recall them for later useinstance ofThis attention span allows the user to feed complex data structures0.80text
long short-term memory or a neural turing machineinstance ofDNCs performed better than alternatives0.80text
Long Short Term Memoryinstance ofand still perform tasks that have longer-term dependencies than some predecessors0.80text
Differentiable neural computerrelated to External linksDifferentiable Neural Network Thinks0.60section
Differentiable neural computerrelated to External linksDeeply0.60section

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