Open Access. Powered by Scholars. Published by Universities.®
Programming Languages and Compilers Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Programming Languages and Compilers
What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth, William Mahoney Luckman
What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth, William Mahoney Luckman
Dissertations, Theses, and Capstone Projects
The word “billion” is a mathematical abstraction related to “big,” but it is difficult to understand the vast difference in value between one million and one billion; even harder to understand the vast difference in purchasing power between one billion dollars, and the average U.S. yearly income. Perhaps most difficult to conceive of is what that purchasing power and huge mass of capital translates to in terms of power. This project blends design, text, facts, and figures into an interactive narrative website that helps the user better understand their position in relation to extreme wealth: https://whatdoesonebilliondollarslooklike.website/
The site incorporates …
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja
Publications and Research
Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source …