Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Context-Free Transductions with Neural Stacks   This paper analyzes the behavior of stack-augmented recurrent neural network
(RNN) models. Due to the architectural similarity between stack RNNs and
pushdown transducers, we train stack RNN models on a number of tasks, including
string reversal, context-free language modelling, and cumulative XOR
evaluation. Examining the behavior of our networks, we show that
stack-augmented RNNs can discover intuitive stack-based strategies for solving
our tasks. However, stack RNNs are more difficult to train than classical
architectures such as LSTMs. Rather than employ stack-based strategies, more
complex networks often find approximate solutions by using the stack as
unstructured memory.

6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys