UROP Project Listing System
 
Project Title:
Detecting Deep Learning Software Defects
Supervisor:CHEUNG Shing Chi
Co-Supervisor:-
Quota:4
Project Description:Deep learning is an emerging computational paradigm widely adopted for smart applications, including autonomous cars, financial technologies, facial identity and data analytics. Recent findings at Stack Overflow and Github reveal that software defects of such applications are common. Yet, such defects are difficult to detect and locate due to the stochastic nature of deep learning networks.
Course type: UROP1100 UROP2100 UROP3100 UROP4100 
Applicant's Roles:Students participating in this project should be competent in Python and TensorFlow programming. They must have a good knowledge of machine learning, in particular, various deep learning models. Students will help construct a bug data set and tests to validate these bugs. They will conduct research to develop automated bug detection techniques for deep learning applications, and even repair the detected bugs automatically. 
Applicant's Learning Objectives:- Advance knowledge in open source deep learning projects. - Improve competence in Python and TensorFlow programming. - Gain knowledge of testing deep learning applications. - Learn and apply research methodology. - Learn how to conduct experimentation. 
Complexity of the project:Challenging 
 



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