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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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dnc memory neural network architecture tasks dncs differentiable using trained model published problem better turing time typically recurrent applications traditional
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| graphs sequentially | instance of | This attention span allows the user to feed complex data structures | 0.80 | text |
| and recall them for later use | instance of | This attention span allows the user to feed complex data structures | 0.80 | text |
| long short-term memory or a neural turing machine | instance of | DNCs performed better than alternatives | 0.80 | text |
| Long Short Term Memory | instance of | and still perform tasks that have longer-term dependencies than some predecessors | 0.80 | text |
| Differentiable neural computer | related to External links | Differentiable Neural Network Thinks | 0.60 | section |
| Differentiable neural computer | related to External links | Deeply | 0.60 | section |
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