Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enabling NAS with Automated Super-Network Generation   Recent Neural Architecture Search (NAS) solutions have produced impressive
results training super-networks and then deriving subnetworks, a.k.a. child
models that outperform expert-crafted models from a pre-defined search space.
Efficient and robust subnetworks can be selected for resource-constrained edge
devices, allowing them to perform well in the wild. However, constructing
super-networks for arbitrary architectures is still a challenge that often
prevents the adoption of these approaches. To address this challenge, we
present BootstrapNAS, a software framework for automatic generation of
super-networks for NAS. BootstrapNAS takes a pre-trained model from a popular
architecture, e.g., ResNet- 50, or from a valid custom design, and
automatically creates a super-network out of it, then uses state-of-the-art NAS
techniques to train the super-network, resulting in subnetworks that
significantly outperform the given pre-trained model. We demonstrate the
solution by generating super-networks from arbitrary model repositories and
make available the resulting super-networks for reproducibility of the results.

20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga