Memory Network Algorithm

A Memory Network Algorithm is a NNet that ...

• Context:
  • It can be implemented by a Memory Network System.
• See: [[]].

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References

2016a

• (Weston, 2016) ⇒ Jason Weston. (2016). “Memory Networks for Language Understanding.” Tutorial at ICML-2016.
  • Memory Networks: General Framework
  • Components: (m, I,G,O,R)
    • A memory [math]\displaystyle{ m }[/math]: an array of objects indexed by m_i
    • Four (potentially learned) components: I, G, O and R:
      • I – input feature map: converts the incoming input to the internal feature representation.
      • G – generalization: updates old memories given the new input.
      • O – output feature map: produces a new output 2, given the new input and the current memory state.
      • R – response: converts the output into the response format desired.

2016b

• (Miller et al., 2016) ⇒ Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston. (2016). “Key-Value Memory Networks for Directly Reading Documents.” In: arXiv Journal, 1606.03126.

2015

• (Sukhbaatar et al., 2015) ⇒ Sainbayar Sukhbaatar, Jason Weston, and Rob Fergus. (2015). “End-to-end Memory Networks.” In: Advances in Neural Information Processing Systems.

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