Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MANAS: Multi-Agent Neural Architecture Search   The Neural Architecture Search (NAS) problem is typically formulated as a
graph search problem where the goal is to learn the optimal operations over
edges in order to maximise a graph-level global objective. Due to the large
architecture parameter space, efficiency is a key bottleneck preventing NAS
from its practical use. In this paper, we address the issue by framing NAS as a
multi-agent problem where agents control a subset of the network and coordinate
to reach optimal architectures. We provide two distinct lightweight
implementations, with reduced memory requirements (1/8th of state-of-the-art),
and performances above those of much more computationally expensive methods.
Theoretically, we demonstrate vanishing regrets of the form O(sqrt(T)), with T
being the total number of rounds. Finally, aware that random search is an,
often ignored, effective baseline we perform additional experiments on 3
alternative datasets and 2 network configurations, and achieve favourable
results in comparison.

32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int