1 citations · 2 across the 6 of their papers we have counts for
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Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
Huiyun Peng, Arjun Gupte, Nicholas John Eliopoulos +8
Energy-efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet o…
What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims
Jason Jones, Wenxin Jiang, Nicholas Synovic +2
Background: Collaborative Software Package Registries (SPRs) are an integral part of the software supply chain. Much engineering work synthesizes SPR package into applications. Pri…
Reusing Deep Learning Models: Challenges and Directions in Software Engineering
James C. Davis, Purvish Jajal, Wenxin Jiang +3
Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive…
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
Wenxin Jiang, Jerin Yasmin, Jason Jones +6
The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their…
PeaTMOSS: Mining Pre-Trained Models in Open-Source Software
Wenxin Jiang, Jason Jones, Jerin Yasmin +6
Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks…
"I see models being a whole other thing": An Empirical Study of Pre-Trained Model Naming Conventions and A Tool for Enhancing Naming Consistency
Wenxin Jiang, Mingyu Kim, Chingwo Cheung +3
As innovation in deep learning continues, many engineers are incorporating Pre-Trained Models (PTMs) as components in computer systems. Some PTMs are foundation models, and others…