Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition
Authors: 
Gustavo Rodrigues dos Reis, 
Adrian Mos, Mario Cortes-Cornax, Cyril LabbéAuthors Info & Claims
Gustavo Rodrigues dos Reis, Adrian Mos, Mario Cortes-Cornax, Cyril LabbéAuthors Info & ClaimsASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Article No.: 144, Pages 1 - 3
Abstract
Machine learning (ML) systems based on deep neural networks are more present than ever in software solutions for numerous industries. Their inner workings relying on models learning with data are as helpful as they are mysterious for non-expert people. There is an increasing need to make the design and development of those solutions accessible to a more general public while at the same time making them easier to explore. In this paper, to address this need, we discuss a proposition of a new assisted approach, centered on the downstream task to be performed, for helping practitioners to start using and applying Deep Learning (DL) techniques. This proposal, supported by an initial testbed UI prototype, uses an externalized form of knowledge, where JSON files compile different pipeline metadata information with their respective related artifacts (e.g., model code, the dataset to be loaded, good hyperparameter choices) that are presented as the user interacts with a conversational agent to suggest candidate solutions for a given task.
References
[1]
Anders Arpteg, Bjorn Brinne, Luka Crnkovic-Friis, and Jan Bosch. 2018. Software Engineering Challenges of Deep Learning. In 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, Prague, 50–59. https://doi.org/10.1109/SEAA.2018.00018
[2]
Ángela Casado and Jónathan Heras. 2018. Guiding the Creation of Deep Learning-based Object Detectors. arXiv:1809.03322 [cs, stat](2018). arxiv:1809.03322http://arxiv.org/abs/1809.03322
[3]
Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar, Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka Žitnik, and Blaž Zupan. 2013. Orange: data mining toolbox in python.
[4]
Danielle Gonzalez, Thomas Zimmermann, and Nachiappan Nagappan. 2020. The State of the ML-Universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub. In Proceedings of the 17th International Conference on Mining Software Repositories (Seoul, Republic of Korea) (MSR ’20). Association for Computing Machinery, New York, NY, USA, 431–442. https://doi.org/10.1145/3379597.3387473
[5]
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org.
[6]
Christian Herta, Benjamin Voigt, Patrick Baumann, Klaus Strohmenger, Christoph Jansen, Oliver Fischer, Gefei Zhang, and Peter Hufnagel. 2019. Deep Teaching: Materials for Teaching Machine and Deep Learning. In Fifth International Conference on Higher Education Advances (2019-04-30). http://ocs.editorial.upv.es/index.php/HEAD/HEAD19/paper/view/9177
[7]
Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, and Yonghui Wu. 2019. Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis. arXiv:1806.04558 [cs, eess] (Jan. 2019). http://arxiv.org/abs/1806.04558 arXiv:1806.04558.
[8]
Steven Lang, Felipe Bravo-Marquez, Christopher Beckham, Mark Hall, and Eibe Frank. 2019. WekaDeeplearning4j: A deep learning package for Weka based on Deeplearning4j. Knowledge-Based Systems 178 (Aug. 2019), 48–50. https://doi.org/10.1016/j.knosys.2019.04.013
[9]
Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, Colin Raffel, Manan Dey, Matthias Gallé, Arun Raja, Chenglei Si, Wilson Y. Lee, Benoît Sagot, and Samson Tan. 2021. Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP. https://doi.org/10.48550/ARXIV.2112.10508
[10]
Keith J. O ’Hara, Douglas Blank, and James Marshall. 2015. Computational Notebooks for AI Education. International Florida Artificial Intelligence Research Society Conference (2015). https://doi.org/10.13140/2.1.2434.5928
[11]
Joaquin Quinonero-Candela. 2009. Dataset shift in machine learning. MIT Press. 229 pages.
[12]
Sebastian Ruder, Matthew E. Peters, Swabha Swayamdipta, and Thomas Wolf. 2019. Transfer Learning in Natural Language Processing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials. Association for Computational Linguistics, Minneapolis, Minnesota, 15–18. https://doi.org/10.18653/v1/N19-5004
[13]
Omar Shaikh, Jon Saad-Falcon, Austin P Wright, Nilaksh Das, Scott Freitas, Omar Isaac Asensio, and Duen Horng Chau. 2021. EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models. arxiv:2103.16435 [cs.LG]
[14]
Xiaoke Shen. 2019. A survey of Object Classification and Detection based on 2D/3D data. arXiv:1905.12683 [cs](2019). arxiv:1905.12683http://arxiv.org/abs/1905.12683
[15]
Sameer Singh and Sebastian Riedel. 2016. Creating Interactive and Visual Educational Resources for AI. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona) (AAAI’16). AAAI Press, 4101–4106.
[16]
El-Ghazali Talbi. 2020. Optimization of deep neural networks: a survey and unified taxonomy. (June 2020). https://hal.inria.fr/hal-02570804
[17]
Ashima Yadav and Dinesh Kumar Vishwakarma. 2020. Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review 53, 6 (2020), 4335–4385. https://doi.org/10.1007/s10462-019-09794-5
Index Terms
- Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition
Index terms have been assigned to the content through auto-classification.
Recommendations
Deep learning: systematic review, models, challenges, and research directions
AbstractThe current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition ...
Deep learning, reinforcement learning, and world models
AbstractDeep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions ...
Applications of game theory in deep learning: a survey
AbstractThis paper provides a comprehensive overview of the applications of game theory in deep learning. Today, deep learning is a fast-evolving area for research in the domain of artificial intelligence. Alternatively, game theory has been showing its ...
Comments
Information & Contributors
Information
Published In
October 2022
2006 pages
ISBN:9781450394758
DOI:10.1145/3551349
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 January 2023
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
ASE '22
ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering
October 10 - 14, 2022
MI, Rochester, USA
Acceptance Rates
Overall Acceptance Rate 82 of 337 submissions, 24%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 79Total Downloads
- Downloads (Last 12 months)23
- Downloads (Last 6 weeks)6
Reflects downloads up to 08 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format