Paper Info
Title
Resource Constrained Neural Network Architecture Search: Will A Submodularity Assumption Help?
Abstract
The design of neural network architectures is frequently either based on human expertise using trial/error and empirical feedback or tackled via large scale reinforcement learning strategies performed over distinct discrete architecture choices. In the latter case, the optimization is often non-differentiable and also not very amenable to derivative-free optimization methods. Most methods in use today require sizable computational resources. And if we want networks that additionally satisfy resource constraints, the above challenges are exacerbated because the search must now balance accuracy with certain budget constraints on resources. We formulate this problem as the optimization of a set function - we find that the empirical behavior of this set function often (but not always) satisfies marginal gain and monotonicity principles - properties central to the idea of submodularity. Based on this observation, we adapt algorithms within discrete optimization to obtain heuristic schemes for neural network architecture search, where we have resource constraints on the architecture. This simple scheme when applied on CIFAR-100 and ImageNet, identifies resource-constrained architectures with quantifiably better performance than current state-of-the-art models designed for mobile devices. Specifically, we find high-performing architectures with fewer parameters and computations by a search method that is much faster.
Year
DOI
Venue
2019
10.1109/ICCV.2019.00199
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)
Field
DocType
Volume
Pattern recognition,Computer science,Neural network architecture,Artificial intelligence
Conference
2019
Issue
ISSN
Citations 
1
1550-5499
1
PageRank 
References 
Authors
0.34
1
3
Name
Order
Citations
PageRank
Yunyang Xiong121.36
Ronak Mehta210.68
Vikas Singh356249.01