Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimizing Recurrent Neural Networks Architectures under Time
  Constraints   Recurrent neural network (RNN)'s architecture is a key factor influencing its
performance. We propose algorithms to optimize hidden sizes under running time
constraint. We convert the discrete optimization into a subset selection
problem. By novel transformations, the objective function becomes submodular
and constraint becomes supermodular. A greedy algorithm with bounds is
suggested to solve the transformed problem. And we show how transformations
influence the bounds. To speed up optimization, surrogate functions are
proposed which balance exploration and exploitation. Experiments show that our
algorithms can find more accurate models or faster models than manually tuned
state-of-the-art and random search. We also compare popular RNN architectures
using our algorithms.

18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin