Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including
computer vision, system configuration, and question-answering. However, DNNs are …

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 …

Software Bills of Materials (SBOMs) have emerged as tools to facilitate the management of
software dependencies, vulnerabilities, licenses, and the supply chain. While significant …

The rapid advancements in pre-trained Large Language Models (LLMs) and Large
Multimodal Models (LMMs) have ushered in a new era of intelligent applications …

Many engineering organizations are reimplementing and extending deep neural networks
from the research community. We describe this process as deep learning model …

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 …

Many software applications incorporate open-source third-party packages distributed by
third-party package registries. Guaranteeing authorship along this supply chain is a …

As innovation in deep learning continues, many engineers want to adopt Pre-Trained deep
learning Models (PTMs) as components in computer systems. PTMs are part of a research-to …

Many software engineers develop, fine-tune, and deploy deep learning (DL) models. They
use DL models in a variety of development frameworks and deploy to a range of runtime …

The development and training of deep learning models have become increasingly costly
and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for …