4 citations · 10 across the 11 of their papers we have counts for
5 papers · 1 filter
An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions
Caleb Tung, Nicholas Eliopoulos, Purvish Jajal +7
Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requireme…
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…
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…
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…