Name
Title | ||
|---|---|---|
On the performance of deep learning for numerical optimization: An application to protein structure prediction |
Abstract | ||
|---|---|---|
Deep neural networks have recently drawn considerable attention to build and evaluate artificial learning models for perceptual tasks. On the other hand, optimization is the problem of selecting a set of element to find an optimal/near optimal criterion. Here, we present a study on the performance of the deep learning models to deal with global optimization problems. More precisely, we would like to learn how to optimize the problems by using the machine learning techniques. Different from proposing very large networks with GPU computational burden and long training time, we focus on searching for lightweight implementations to find the best architecture. The performance of NAS is first analyzed through empirical experiments on CEC 2017 benchmark suite. Thereafter, it is applied to a set of protein structure prediction (PSP) problems. The experiments reveal that the generated learning models can achieve competitive results when compared to hand-designed algorithms; given enough computational budget. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.asoc.2021.107596 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Neural architecture search, Optimization, Deep learning, Convolutional neural network | Journal | 110 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hojjat Rakhshani | 1 | 0 | 1.35 |
| Lhassane Idoumghar | 2 | 145 | 25.07 |
| Soheila Ghambari | 3 | 0 | 0.68 |
| Julien Lepagnot | 4 | 308 | 19.88 |
| Mathieu Brévilliers | 5 | 0 | 0.68 |