end-to-end memory networks

end-to-end memory networks

Sukhbaatar, S., Weston, J., & Fergus, R. (2015). End-to-end memory networks. In Advances in neural information processing systems (pp. 2440-2448).

$ m_i = \sum_j Ax_{ij}

The question text q is embedded as well

$ u = \sum_j Bq_j

The importance p of each of the N stored information to u is the soft max of the inner product

$ p_i = \mathrm{softmax}(u \cdot m_i)

$ o= p \cdot \sum_j Cx_{ij}

$ \hat{a} = \mathrm{softmax} (W(o+u))

Is such a design acceptable in many ways? However, the constraint that the entire system must be differentiable in order to optimize end-to-end is severe.

https://gyazo.com/87e61c10f3997c3f3b7fe412b2b08aea

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