Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition
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.
Thu 13 OctDisplayed time zone: Eastern Time (US & Canada) change
10:00 - 12:00 | Technical Session 21 - SE for AI IIResearch Papers / Late Breaking Results / NIER Track / Journal-first Papers at Banquet B Chair(s): Andrea Stocco Università della Svizzera italiana (USI) | ||
10:00 20mResearch paper | DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks Research Papers Simin Chen University of Texas at Dallas, USA, Mirazul Haque UT Dallas, Cong Liu University of Texas at Dallas, USA, Wei Yang University of Texas at Dallas | ||
10:20 10mPaper | Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition Late Breaking Results Gustavo Rodrigues dos Reis Rodrigues dos Reis, Adrian Mos NAVER LABS Europe, Cyril Labbé LIG - UGA, Mario Cortes Cornax LIG - UGA | ||
10:30 20mResearch paper | Boosting the Revealing of Detected Violations in Deep Learning Testing: A Diversity-Guided MethodVirtualACM SIGSOFT Distinguished Paper Award Research Papers Xiaoyuan Xie School of Computer Science, Wuhan University, China, Pengbo Yin School of Computer Science, Wuhan University, Songqiang Chen School of Computer Science, Wuhan University | ||
10:50 20mPaper | Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachVirtual Journal-first Papers Amin Nikanjam École Polytechnique de Montréal, Mohammad Mehdi Morovati École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal, Houssem Ben Braiek École Polytechnique de Montréal Link to publication DOI Authorizer link | ||
11:10 20mResearch paper | Towards Understanding the Faults of JavaScript-Based Deep Learning SystemsVirtual Research Papers Lili Quan Tianjin University, Qianyu Guo College of Intelligence and Computing, Tianjin University, Xiaofei Xie Singapore Management University, Singapore, Sen Chen Tianjin University, Xiaohong Li TianJin University, Yang Liu Nanyang Technological University | ||
11:30 10mVision and Emerging Results | An Empirical Study on Numerical Bugs in Deep Learning ProgramsVirtual NIER Track Gan Wang , Zan Wang Tianjin University, China, Junjie Chen Tianjin University, Xiang Chen Nantong University, Ming Yan College of Intelligence and Computing, Tianjin University | ||
11:40 20mResearch paper | Toward Improving the Robustness of Deep Learning Models via Model TransformationVirtual Research Papers Yingyi Zhang College of Intelligence and Computing, Tianjin University, Zan Wang Tianjin University, China, Jiajun Jiang Tianjin University, Hanmo You College of Intelligence and Computing, Tianjin University, Junjie Chen Tianjin University | ||