Zobrazeno 1 - 10
of 494
pro vyhledávání: '"Jiang Wenxin"'
Publikováno v:
Journal of the American College of Emergency Physicians Open, Vol 5, Iss 5, Pp n/a-n/a (2024)
Externí odkaz:
https://doaj.org/article/bc89015ef6a5445cb1fff13a5a5a5fa8
Publikováno v:
Zhongguo shipin weisheng zazhi, Vol 35, Iss 4, Pp 600-606 (2023)
To provide references for reform of health food market access system in China， the filing system and food products with function claims in Japan are studied. The differences of the laws and regulations of the filing system and food products with fu
Externí odkaz:
https://doaj.org/article/2126ff5c32804e9ca941d9594633b581
Autor:
Peng, Huiyun, Gupte, Arjun, Eliopoulos, Nicholas John, Ho, Chien Chou, Mantri, Rishi, Deng, Leo, Jiang, Wenxin, Lu, Yung-Hsiang, Läufer, Konstantin, Thiruvathukal, George K., Davis, James C.
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 other requirements. We take inspiration from recent work exploring the
Externí odkaz:
http://arxiv.org/abs/2410.09241
Due to the high dimensionality or multimodality that is common in modern astronomy, sampling Bayesian posteriors can be challenging. Several publicly available codes based on different sampling algorithms can solve these complex models, but the execu
Externí odkaz:
http://arxiv.org/abs/2407.09915
Background: Collaborative Software Package Registries (SPRs) are an integral part of the software supply chain. Much engineering work synthesizes SPR package into applications. Prior research has examined SPRs for traditional software, such as NPM (J
Externí odkaz:
http://arxiv.org/abs/2406.08205
This paper undertakes the task of replicating the MaskFormer model a universal image segmentation model originally developed using the PyTorch framework, within the TensorFlow ecosystem, specifically optimized for execution on Tensor Processing Units
Externí odkaz:
http://arxiv.org/abs/2404.18801
Autor:
Davis, James C., Jajal, Purvish, Jiang, Wenxin, Schorlemmer, Taylor R., Synovic, Nicholas, Thiruvathukal, George K.
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 to develop, both in intellectual effort (e.g., devising new architect
Externí odkaz:
http://arxiv.org/abs/2404.16688
Autor:
Jiang, Wenxin, Yasmin, Jerin, Jones, Jason, Synovic, Nicholas, Kuo, Jiashen, Bielanski, Nathaniel, Tian, Yuan, Thiruvathukal, George K., Davis, James C.
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 downstream applications. The dynamics of the PTM supply chain remain l
Externí odkaz:
http://arxiv.org/abs/2402.00699
Autor:
Jiang, Wenxin, Jones, Jason, Yasmin, Jerin, Synovic, Nicholas, Sashti, Rajeev, Chen, Sophie, Thiruvathukal, George K., Tian, Yuan, Davis, James C.
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. Despite the wide-spread use of PTMs, we know little about the corres
Externí odkaz:
http://arxiv.org/abs/2310.03620
Autor:
Jiang, Wenxin, Cheung, Chingwo, Kim, Mingyu, Kim, Heesoo, Thiruvathukal, George K., Davis, James C.
As innovation in deep learning continues, many engineers seek to adopt Pre-Trained Models (PTMs) as components in computer systems. Researchers publish PTMs, which engineers adapt for quality or performance prior to deployment. PTM authors should cho
Externí odkaz:
http://arxiv.org/abs/2310.01642