Zobrazeno 1 - 10
of 2 958
pro vyhledávání: '"ZHANG, WENBIN"'
Federated learning (FL), an emerging distributed machine learning paradigm, has been applied to various privacy-preserving scenarios. However, due to its distributed nature, FL faces two key issues: the non-independent and identical distribution (non
Externí odkaz:
http://arxiv.org/abs/2410.13083
Autor:
Liu, Jun, Yuan, Geng, Zeng, Weihao, Tang, Hao, Zhang, Wenbin, Lin, Xue, Xu, XiaoLin, Huang, Dong, Wang, Yanzhi
Publikováno v:
Springer Nature - Book Series: Transactions on Computational Science & Computational Intelligence, 2022
In research findings, co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. The ability to predict 1p19q status is critical for treatment planning and patient follow-up. This study aims to utilize a specially MRI-b
Externí odkaz:
http://arxiv.org/abs/2409.19583
In this letter, we analyze the performance of mixed coherent and non-coherent transmissions approach, which can improve the performance of cell-free multiple-input multiple-output orthogonal frequency division multiplexing (CF mMIMO-OFDM) systems und
Externí odkaz:
http://arxiv.org/abs/2408.12329
Large Language Models (LLMs) have demonstrated remarkable success across various domains but often lack fairness considerations, potentially leading to discriminatory outcomes against marginalized populations. Unlike fairness in traditional machine l
Externí odkaz:
http://arxiv.org/abs/2408.00992
Autor:
Chinta, Sribala Vidyadhari, Wang, Zichong, Zhang, Xingyu, Viet, Thang Doan, Kashif, Ayesha, Smith, Monique Antoinette, Zhang, Wenbin
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have signif
Externí odkaz:
http://arxiv.org/abs/2407.19655
Autor:
Chinta, Sribala Vidyadhari, Wang, Zichong, Yin, Zhipeng, Hoang, Nhat, Gonzalez, Matthew, Quy, Tai Le, Zhang, Wenbin
The integration of Artificial Intelligence (AI) into education has transformative potential, providing tailored learning experiences and creative instructional approaches. However, the inherent biases in AI algorithms hinder this improvement by unint
Externí odkaz:
http://arxiv.org/abs/2407.18745
Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, l
Externí odkaz:
http://arxiv.org/abs/2407.18454
3D object detection plays an important role in autonomous driving; however, its vulnerability to backdoor attacks has become evident. By injecting ''triggers'' to poison the training dataset, backdoor attacks manipulate the detector's prediction for
Externí odkaz:
http://arxiv.org/abs/2405.03884
Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recoverin
Externí odkaz:
http://arxiv.org/abs/2404.09430
Autor:
Wang, Jingying, Gong, Dawei, Luo, Huichun, Zhang, Wenbin, Zhang, Lei, Zhang, Han, Zhou, Junhong, Wang, Shouyan
Publikováno v:
JMIR mHealth and uHealth, Vol 8, Iss 3, p e16650 (2020)
BackgroundGait impairments including shuffling gait and hesitation are common in people with Parkinson’s disease (PD), and have been linked to increased fall risk and freezing of gait. Nowadays the gait metrics mostly focus on the spatiotemporal ch
Externí odkaz:
https://doaj.org/article/9d985117d5cd49bc99ea61a63b680573
