Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Key-Value Memory to Flexibly Adjust Redundancy in
  Memory-Augmented Networks   Memory-augmented neural networks enhance a neural network with an external
key-value memory whose complexity is typically dominated by the number of
support vectors in the key memory. We propose a generalized key-value memory
that decouples its dimension from the number of support vectors by introducing
a free parameter that can arbitrarily add or remove redundancy to the key
memory representation. In effect, it provides an additional degree of freedom
to flexibly control the trade-off between robustness and the resources required
to store and compute the generalized key-value memory. This is particularly
useful for realizing the key memory on in-memory computing hardware where it
exploits nonideal, but extremely efficient non-volatile memory devices for
dense storage and computation. Experimental results show that adapting this
parameter on demand effectively mitigates up to 44% nonidealities, at equal
accuracy and number of devices, without any need for neural network retraining.

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The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
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The categories of this paper are: plasm-ph