Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Memory Update   Recurrent neural networks are important tools for sequential data processing.
However, they are notorious for problems regarding their training. Challenges
include capturing complex relations between consecutive states and stability
and efficiency of training. In this paper, we introduce a recurrent neural
architecture called Deep Memory Update (DMU). It is based on updating the
previous memory state with a deep transformation of the lagged state and the
network input. The architecture is able to learn to transform its internal
state using any nonlinear function. Its training is stable and fast due to
relating its learning rate to the size of the module. Even though DMU is based
on standard components, experimental results presented here confirm that it can
compete with and often outperform state-of-the-art architectures such as Long
Short-Term Memory, Gated Recurrent Units, and Recurrent Highway Networks.

23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc