5 citations · 18 across the 18 of their papers we have counts for
19 papers
VeriPort: Automated and Verified Patch Backporting at Scale
Jonah Ghebremichael, Wenxin Jiang, Mikola Lysenko +3
One of the key challenges for securing the software supply chain is addressing known vulnerabilities in third-party open-source dependencies. Security patches are frequently only a…
Debiasing LLMs by Fine-tuning
Zhenyu Gao, Wenxi Jiang, Yutong Yan
Prior research shows that large language models (LLMs) exhibit systematic extrapolation bias when forming predictions from both experimental and real-world data, and that prompt-ba…
DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining
Yutong Yan, Raphael Tang, Zhenyu Gao +2
Large language models pretrained on internet-scale data risk lookahead bias in forecasting tasks, as they may have already seen the true outcome during training. To address this, w…
AgentHub: A Registry for Discoverable, Verifiable, and Reproducible AI Agents
Erik Pautsch, Tanmay Singla, Parv Kumar +6
LLM-based agents are rapidly proliferating, yet the infrastructure for discovering, evaluating, and governing them remains fragmented compared to mature ecosystems like software pa…
PickleBall: Secure Deserialization of Pickle-based Machine Learning Models (Extended Report)
Andreas D. Kellas, Neophytos Christou, Wenxin Jiang +6
Machine learning model repositories such as the Hugging Face Model Hub facilitate model exchanges. However, bad actors can deliver malware through compromised models. Existing defe…
ConfuGuard: Using Metadata to Detect Active and Stealthy Package Confusion Attacks Accurately and at Scale
Wenxin Jiang, Berk Çakar, Mikola Lysenko +1
Package confusion attacks such as typosquatting threaten software supply chains. Attackers make packages with names that syntactically or semantically resemble legitimate ones, tri…
Recommending Pre-Trained Models for IoT Devices
Parth V. Patil, Wenxin Jiang, Huiyun Peng +7
The availability of pre-trained models (PTMs) has enabled faster deployment of machine learning across applications by reducing the need for extensive training. Techniques like qua…
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…
A Partial Replication of MaskFormer in TensorFlow on TPUs for the TensorFlow Model Garden
Vishal Purohit, Wenxin Jiang, Akshath R. Ravikiran +1
This paper undertakes the task of replicating the MaskFormer model a universal image segmentation model originally developed using the PyTorch framework, within the TensorFlow ecos…
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…
"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…
An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors
Vishnu Banna, Akhil Chinnakotla, Zhengxin Yan +7
Machine learning techniques are becoming a fundamental tool for scientific and engineering progress. These techniques are applied in contexts as diverse as astronomy and spam filte…