Zobrazeno 1 - 10
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pro vyhledávání: '"WANG Xinran"'
Autor:
Wang, Xinran, Le, Qi, Ahmed, Ammar, Diao, Enmao, Zhou, Yi, Baracaldo, Nathalie, Ding, Jie, Anwar, Ali
Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the d
Externí odkaz:
http://arxiv.org/abs/2410.19198
Federated Learning (FL) is a collaborative machine learning framework that allows multiple users to train models utilizing their local data in a distributed manner. However, considerable statistical heterogeneity in local data across devices often le
Externí odkaz:
http://arxiv.org/abs/2409.04986
Autor:
Zhang, Haiwen, Yang, Zixi, Liu, Yuanzhi, Wang, Xinran, He, Zheqi, Liang, Kongming, Ma, Zhanyu
Currently, large vision-language models have gained promising progress on many downstream tasks. However, they still suffer many challenges in fine-grained visual understanding tasks, such as object attribute comprehension. Besides, there have been g
Externí odkaz:
http://arxiv.org/abs/2408.13898
Representing signals using coordinate networks dominates the area of inverse problems recently, and is widely applied in various scientific computing tasks. Still, there exists an issue of spectral bias in coordinate networks, limiting the capacity t
Externí odkaz:
http://arxiv.org/abs/2407.17834
Autor:
Zhang, Hanhao, Wei, Yuanhao, Li, Yuhao, Lin, Shengsheng, Wang, Jiarui, Taniguchi, Takashi, Watanabe, Kenji, Li, Jiangyu, Shi, Yi, Wang, Xinran, Shi, Yan, Fei, Zaiyao
The coupling of mechanical deformation and electrical stimuli at the nanoscale has been a subject of intense investigation in the realm of materials science. Recently, twisted van der Waals (vdW) materials have emerged as a platform to explore exotic
Externí odkaz:
http://arxiv.org/abs/2406.11442
A primary function of back-propagation is to compute both the gradient of hidden representations and parameters for optimization with gradient descent. Training large models requires high computational costs due to their vast parameter sizes. While P
Externí odkaz:
http://arxiv.org/abs/2404.13844
Autor:
Wang, Xinran, Rojas, Nicolas
This paper introduces a Cosserat rod based mathematical model for modeling a self-controllable variable curvature soft continuum robot. This soft continuum robot has a hollow inner channel and was developed with the ability to perform variable curvat
Externí odkaz:
http://arxiv.org/abs/2402.12315
Publikováno v:
IEEE Robotics and Automation Letters 2024
This paper introduces a new type of soft continuum robot, called SCoReS, which is capable of self-controlling continuously its curvature at the segment level; in contrast to previous designs which either require external forces or machine elements, o
Externí odkaz:
http://arxiv.org/abs/2401.01739
Autor:
Liang, Kongming, Wang, Xinran, Wang, Rui, Gao, Donghui, Jin, Ling, Liu, Weidong, Zhu, Xiatian, Ma, Zhanyu, Guo, Jun
Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization. Existing attribute learning methods often treat the missing labels as negative or simply ignore them all during training, eith
Externí odkaz:
http://arxiv.org/abs/2312.07009
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 10, pp. 3731-3763.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-02-2024-0136
