Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MiLeNAS: Efficient Neural Architecture Search via Mixed-Level
  Reformulation   Many recently proposed methods for Neural Architecture Search (NAS) can be
formulated as bilevel optimization. For efficient implementation, its solution
requires approximations of second-order methods. In this paper, we demonstrate
that gradient errors caused by such approximations lead to suboptimality, in
the sense that the optimization procedure fails to converge to a (locally)
optimal solution. To remedy this, this paper proposes \mldas, a mixed-level
reformulation for NAS that can be optimized efficiently and reliably. It is
shown that even when using a simple first-order method on the mixed-level
formulation, \mldas\ can achieve a lower validation error for NAS problems.
Consequently, architectures obtained by our method achieve consistently higher
accuracies than those obtained from bilevel optimization. Moreover, \mldas\
proposes a framework beyond DARTS. It is upgraded via model size-based search
and early stopping strategies to complete the search process in around 5 hours.
Extensive experiments within the convolutional architecture search space
validate the effectiveness of our approach.

3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an