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Found 5 papers, 3 papers with code
Training Green AI Models Using Elite Samples
The substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices.
A Systematic Review of Green AI
As a conclusion, the Green AI research field results to have reached a considerable level of maturity.
Data-Centric Green AI: An Exploratory Empirical Study
Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92. 16%), often at the cost of a negligible or even absent accuracy decline.
Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study
Results: Our results show that (i) established TD types, variations of them, and four new TD types (data, model, configuration, and ethics debt) are present in AI-based systems, (ii) 72 antipatterns are discussed in the literature, the majority related to data and model deficiencies, and (iii) 46 solutions have been proposed, either to address specific TD types, antipatterns, or TD in general.
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits.
Software Engineering
