Name
Abstract | ||
|---|---|---|
(DL)We present a novel approach to rank Deep Learning hyper-parameters through the application of Sensitivity Analysis (SA). DL hyper-parameter tuning is crucial to model accuracy however, choosing optimal values for each parameter is time and resource-intensive. SA provides a quantitative measure by which hyper-parameters can be ranked in terms of contribution to model accuracy. Learning rate dec... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICTAI52525.2021.00083 | 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | DocType | ISSN |
Sensitivity Analysis,Deep Learning,Hyperparameter Tuning,Hyper-parameter rank,Hyper-parameter Influence | Conference | 1082-3409 |
ISBN | Citations | PageRank |
978-1-6654-0898-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rhian Taylor | 1 | 0 | 0.34 |
| Varun Kumar Ojha | 2 | 32 | 9.25 |
| Martino Ivan | 3 | 0 | 1.01 |
| Giuseppe Nicosia | 4 | 0 | 1.69 |