Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-18993| Publication type: | Conference paper |
| Type of review: | Peer review (abstract) |
| Title: | Hyperparameter tuning for deep learning in natural language processing |
| Authors: | Aghaebrahimian, Ahmad Cieliebak, Mark |
| et. al: | No |
| DOI: | 10.21256/zhaw-18993 |
| Conference details: | 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019 |
| Issue Date: | 2019 |
| Publisher / Ed. Institution: | Swisstext |
| Language: | English |
| Subject (DDC): | 006: Special computer methods |
| Abstract: | Deep Neural Networks have advanced rapidly over the past several years. However, it still seems like a black art for many people to make use of them efficiently. The reason for this complexity is that obtaining a consistent and outstanding result from a deep architecture requires optimizing many parameters known as hyperparameters. Hyperparameter tuning is an essential task in deep learning, which can make significant changes in network performance. This paper is the essence of over 3000 GPU hours on optimizing a network for a text classification task on a wide array of hyperparameters. We provide a list of hyperparameters to tune in addition to their tuning impact on the network performance. The hope is that such a listing will provide the interested researchers a mean to prioritize their efforts and to modify their deep architecture for getting the best performance with the least effort. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/18993 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Not specified |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| swisstext19_Hyperparameters.pdf | Hyperparameter Tuning for Deep Learning in Natural Language Processing | 171.02 kB | Adobe PDF |  View/Open |
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Aghaebrahimian, A., & Cieliebak, M. (2019). Hyperparameter tuning for deep learning in natural language processing. 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019. https://doi.org/10.21256/zhaw-18993
Aghaebrahimian, A. and Cieliebak, M. (2019) ‘Hyperparameter tuning for deep learning in natural language processing’, in 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019. Swisstext. Available at: https://doi.org/10.21256/zhaw-18993.
A. Aghaebrahimian and M. Cieliebak, “Hyperparameter tuning for deep learning in natural language processing,” in 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019, 2019. doi: 10.21256/zhaw-18993.
AGHAEBRAHIMIAN, Ahmad und Mark CIELIEBAK, 2019. Hyperparameter tuning for deep learning in natural language processing. In: 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019. Conference paper. Swisstext. 2019
Aghaebrahimian, Ahmad, and Mark Cieliebak. 2019. “Hyperparameter Tuning for Deep Learning in Natural Language Processing.” Conference paper. In 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019. Swisstext. https://doi.org/10.21256/zhaw-18993.
Aghaebrahimian, Ahmad, and Mark Cieliebak. “Hyperparameter Tuning for Deep Learning in Natural Language Processing.” 4th Swiss Text Analytics Conference (SwissText 2019), Winterthur, June 18-19 2019, Swisstext, 2019, https://doi.org/10.21256/zhaw-18993.
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