5 citations · 18 across the 14 of their papers we have counts for
14 papers
Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis
Nikita Ravi, Abhinav Goel, James C. Davis +1
The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside…
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…
PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages
Wenxin Jiang, Nicholas Synovic, Purvish Jajal +5
Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downst…
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
Wenxin Jiang, Nicholas Synovic, Matt Hyatt +5
Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art ar…
Discrepancies among Pre-trained Deep Neural Networks: A New Threat to Model Zoo Reliability
Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz +3
Training deep neural networks (DNNs) takes signifcant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model z…
Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
Purvish Jajal, Wenxin Jiang, Arav Tewari +6
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models betw…
Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision
Wenxin Jiang, Vishnu Banna, Naveen Vivek +4
Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Dee…
Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics
Nicholas Synovic, Matt Hyatt, Rohan Sethi +11
Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. How…
Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks
Abhinav Goel, Caleb Tung, Xiao Hu +3
Computer vision on low-power edge devices enables applications including search-and-rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural Networks (…