Design patterns for resource-constrained automated deep-learning methods

Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar; Schilling, Frank-Peter; Stadelmann, Thilo

doi:10.3390/ai1040031

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20804

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Design patterns for resource-constrained automated deep-learning methods
Authors:	Tuggener, Lukas Amirian, Mohammadreza Benites de Azevedo e Souza, Fernando von Däniken, Pius Gupta, Prakhar Schilling, Frank-Peter Stadelmann, Thilo
et. al:	No
DOI:	10.3390/ai1040031 10.21256/zhaw-20804
Published in:	AI
Volume(Issue):	1
Issue:	4
Page(s):	510
Pages to:	538
Issue Date:	6-Nov-2020
Publisher / Ed. Institution:	MDPI
ISSN:	2673-2688
Language:	English
Subjects:	Automated machine learning; Architecture design; Computer vision; Audio processing; Natural language processing; Weakly supervised learning
Subject (DDC):	006: Special computer methods
Abstract:	We present an extensive evaluation of a wide variety of promising design patterns for automated deep-learning (AutoDL) methods, organized according to the problem categories of the 2019 AutoDL challenges, which set the task of optimizing both model accuracy and search efficiency under tight time and computing constraints. We propose structured empirical evaluations as the most promising avenue to obtain design principles for deep-learning systems due to the absence of strong theoretical support. From these evaluations, we distill relevant patterns which give rise to neural network design recommendations. In particular, we establish (a) that very wide fully connected layers learn meaningful features faster; we illustrate (b) how the lack of pretraining in audio processing can be compensated by architecture search; we show (c) that in text processing deep-learning-based methods only pull ahead of traditional methods for short text lengths with less than a thousand characters under tight resource limitations; and lastly we present (d) evidence that in very data- and computing-constrained settings, hyperparameter tuning of more traditional machine-learning methods outperforms deep-learning systems.
URI:	https://digitalcollection.zhaw.ch/handle/11475/20804
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project:	Ada – Advanced Algorithms for an Artificial Data Analyst
Appears in collections:	Publikationen School of Engineering

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Tuggener, L., Amirian, M., Benites de Azevedo e Souza, F., von Däniken, P., Gupta, P., Schilling, F.-P., & Stadelmann, T. (2020). Design patterns for resource-constrained automated deep-learning methods. Ai, 1(4), 510–538. https://doi.org/10.3390/ai1040031

Tuggener, L. et al. (2020) ‘Design patterns for resource-constrained automated deep-learning methods’, AI, 1(4), pp. 510–538. Available at: https://doi.org/10.3390/ai1040031.

L. Tuggener et al., “Design patterns for resource-constrained automated deep-learning methods,” AI, vol. 1, no. 4, pp. 510–538, Nov. 2020, doi: 10.3390/ai1040031.

TUGGENER, Lukas, Mohammadreza AMIRIAN, Fernando BENITES DE AZEVEDO E SOUZA, Pius VON DÄNIKEN, Prakhar GUPTA, Frank-Peter SCHILLING und Thilo STADELMANN, 2020. Design patterns for resource-constrained automated deep-learning methods. AI. 6 November 2020. Bd. 1, Nr. 4, S. 510–538. DOI 10.3390/ai1040031

Tuggener, Lukas, Mohammadreza Amirian, Fernando Benites de Azevedo e Souza, Pius von Däniken, Prakhar Gupta, Frank-Peter Schilling, and Thilo Stadelmann. 2020. “Design Patterns for Resource-Constrained Automated Deep-Learning Methods.” Ai 1 (4): 510–38. https://doi.org/10.3390/ai1040031.

Tuggener, Lukas, et al. “Design Patterns for Resource-Constrained Automated Deep-Learning Methods.” Ai, vol. 1, no. 4, Nov. 2020, pp. 510–38, https://doi.org/10.3390/ai1040031.