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Found 12 papers, 3 papers with code
Lessons Learned from Mining the Hugging Face Repository
Our objective is to provide a practical guide for future researchers embarking on mining software repository studies within the HF ecosystem to enhance the quality of these studies.
Towards green AI-based software systems: an architecture-centric approach (GAISSA)
Nowadays, AI-based systems have achieved outstanding results and have outperformed humans in different domains.
Do DL models and training environments have an impact on energy consumption?
This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models.
Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study
This study seeks to answer two research questions: (1) how do ML model creators measure and report carbon emissions on Hugging Face Hub?, and (2) what aspects impact the carbon emissions of training ML models?
Energy Efficiency of Training Neural Network Architectures: An Empirical Study
The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures.
Guiding the retraining of convolutional neural networks against adversarial inputs
Our goal is to improve the models against adversarial inputs regarding accuracy, resource utilization and time from the point of view of a data scientist in the context of image classification.
Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?
Overall, we plan to model the accuracy and complexity of AI-enabled applications in operation with respect to their design decisions and will provide tools for allowing practitioners to gain consciousness of the quantitative relationship between the design decisions and the green characteristics of study.
Software-Based Dialogue Systems: Survey, Taxonomy and Challenges
The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research.
Software Engineering for AI-Based Systems: A Survey
Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
Integration of Convolutional Neural Networks in Mobile Applications
In this paper, we study the performance of a system that integrates a DL model as a trade-off between the accuracy and the complexity.
QFL: Data-Driven Feedback Loop to Manage Quality in Agile Development
As a result of the implementation of QFL in Softeam, Modelio's team members highlight the benefits of integrating a data analytics tool with their project planning tool and the fact that project managers can control the whole process making the final decisions.
Software Engineering
Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems
Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment.
