Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Neural Language Models with a Continuous Cache   We propose an extension to neural network language models to adapt their
prediction to the recent history. Our model is a simplified version of memory
augmented networks, which stores past hidden activations as memory and accesses
them through a dot product with the current hidden activation. This mechanism
is very efficient and scales to very large memory sizes. We also draw a link
between the use of external memory in neural network and cache models used with
count based language models. We demonstrate on several language model datasets
that our approach performs significantly better than recent memory augmented
networks.

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The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin