Neural Turing machine

A Neural Turing machine (NTMs) is a recurrent neural network model published by Alex Graves et. al. in 2014. NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers. An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent.[1] An NTM with a long short-term memory (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.[2]

They can infer algorithms from input and output examples alone.

Differentiable neural computers are an outgrowth of neural Turing machines, with attention mechanisms that control where the memory is active, and improved performance.[3]

See also

References

  1. ↑ "Deep Minds: An Interview with Google's Alex Graves & Koray Kavukcuoglu". Retrieved May 17, 2016.
  2. ↑ Graves, Alex; Wayne, Greg; Danihelka, Ivo (2014). "Neural Turing Machines". arXiv:1410.5401 Freely accessible [cs.NE].
  3. ↑ Administrator. "DeepMind's Differentiable Neural Network Thinks Deeply". www.i-programmer.info. Retrieved 2016-10-20.
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