Zobrazeno 1 - 10
of 2 394
pro vyhledávání: '"Davis, James P."'
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
Vehicle object detection is possible using both LiDAR and camera data. Methods using LiDAR generally outperform those using cameras only. The highest accuracy methods utilize both of these modalities through data fusion. In our study, we propose a mo
Externí odkaz:
http://arxiv.org/abs/2409.15529
Autor:
Jajal, Purvish, Eliopoulos, Nick John, Chou, Benjamin Shiue-Hal, Thiravathukal, George K., Davis, James C., Lu, Yung-Hsiang
We propose Vision Token Turing Machines (ViTTM), an efficient, low-latency, memory-augmented Vision Transformer (ViT). Our approach builds on Neural Turing Machines and Token Turing Machines, which were applied to NLP and sequential visual understand
Externí odkaz:
http://arxiv.org/abs/2409.07613
This paper presents an innovative data-centric paradigm for designing computational systems by introducing a new informatics domain model. The proposed model moves away from the conventional node-centric framework and focuses on data-centric categori
Externí odkaz:
http://arxiv.org/abs/2409.09058
Autor:
Liu, Minghao, Di, Zonglin, Wei, Jiaheng, Wang, Zhongruo, Zhang, Hengxiang, Xiao, Ruixuan, Wang, Haoyu, Pang, Jinlong, Chen, Hao, Shah, Ankit, Wei, Hongxin, He, Xinlei, Zhao, Zhaowei, Wang, Haobo, Feng, Lei, Wang, Jindong, Davis, James, Liu, Yang
Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating high-quality datasets quickly and accurately remains a challenge due t
Externí odkaz:
http://arxiv.org/abs/2408.11338
For nearly a century, mathematicians have been developing techniques for constructing abelian automorphism groups of combinatorial objects, and, conversely, constructing combinatorial objects from abelian groups. While abelian groups are a natural pl
Externí odkaz:
http://arxiv.org/abs/2407.18385
Autor:
Schorlemmer, Taylor R., Burmane, Ethan H., Kalu, Kelechi G., Torres-Arias, Santiago, Davis, James C.
Software engineers integrate third-party components into their applications. The resulting software supply chain is vulnerable. To reduce the attack surface, we can verify the origin of components (provenance) before adding them. Cryptographic signat
Externí odkaz:
http://arxiv.org/abs/2407.03949
Autor:
Eliopoulos, Nick John, Jajal, Purvish, Davis, James, Liu, Gaowen, Thiravathukal, George K., Lu, Yung-Hsiang
This paper investigates how to efficiently deploy vision transformers on edge devices for small workloads. Recent methods reduce the latency of transformer neural networks by removing or merging tokens, with small accuracy degradation. However, these
Externí odkaz:
http://arxiv.org/abs/2407.05941
Code signing enables software developers to digitally sign their code using cryptographic keys, thereby associating the code to their identity. This allows users to verify the authenticity and integrity of the software, ensuring it has not been tampe
Externí odkaz:
http://arxiv.org/abs/2406.15596
Autor:
Franke, Lucas, Liang, Huayu, Farzanehpour, Sahar, Brantly, Aaron, Davis, James C., Brown, Chris
Background: Governments worldwide are considering data privacy regulations. These laws, e.g. the European Union's General Data Protection Regulation (GDPR), require software developers to meet privacy-related requirements when interacting with users'
Externí odkaz:
http://arxiv.org/abs/2406.14724