Memory Network Algorithm: Difference between revisions

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=== 2016a ===
=== 2016a ===
* ([[2016_MemoryNetworksforLanguageUnders|Weston, 2016]]) ⇒ [[Jason Weston]]. ([[2016]]). “Memory Networks for Language Understanding.” Tutorial at [[ICML-2016]].  
* ([[2016_MemoryNetworksforLanguageUnders|Weston, 2016]]) ⇒ [[Jason Weston]]. ([[2016]]). “Memory Networks for Language Understanding.” Tutorial at [[ICML-2016]].
** Memory Networks:  General Framework
** Memory Networks:  General Framework
** Components: (m, I,G,O,R)
** Components: (m, I,G,O,R)
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=== 2015 ===
=== 2015 ===
* ([[2015_EndtoEndMemoryNetworks|Sukhbaatar et al., 2015]]) ⇒ [[Sainbayar Sukhbaatar]], [[Jason Weston]], and [[Rob Fergus]]. ([[2015]]). “[http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf End-to-end Memory Networks].” In: Advances in Neural Information Processing Systems.  
* ([[2015_EndtoEndMemoryNetworks|Sukhbaatar et al., 2015]]) ⇒ [[Sainbayar Sukhbaatar]], [[Jason Weston]], and [[Rob Fergus]]. ([[2015]]). “[http://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf End-to-end Memory Networks].” In: Advances in Neural Information Processing Systems.


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Revision as of 13:13, 2 August 2022

A Memory Network Algorithm is a NNet that ...



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

2015