Zobrazeno 1 - 10
of 2 113
pro vyhledávání: '"LIU, Yuxuan"'
In-Context Operator Networks (ICONs) are models that learn operators across different types of PDEs using a few-shot, in-context approach. Although they show successful generalization to various PDEs, existing methods treat each data point as a singl
Externí odkaz:
http://arxiv.org/abs/2411.16063
Autor:
Mou, Shancong, Vemulapalli, Raviteja, Li, Shiyu, Liu, Yuxuan, Thomas, C, Cao, Meng, Bai, Haoping, Tuzel, Oncel, Huang, Ping, Shan, Jiulong, Shi, Jianjun
Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect data generation is a popular approach for mitigating data challenges. How
Externí odkaz:
http://arxiv.org/abs/2410.18490
Large language models (LLMs) often face a bottleneck in inference speed due to their reliance on auto-regressive decoding. Recently, parallel decoding has shown significant promise in enhancing inference efficiency. However, we have identified two ke
Externí odkaz:
http://arxiv.org/abs/2410.13344
Road curbs are considered as one of the crucial and ubiquitous traffic features, which are essential for ensuring the safety of autonomous vehicles. Current methods for detecting curbs primarily rely on camera imagery or LiDAR point clouds. Image-bas
Externí odkaz:
http://arxiv.org/abs/2409.20171
Traffic sign is a critical map feature for navigation and traffic control. Nevertheless, current methods for traffic sign recognition rely on traditional deep learning models, which typically suffer from significant performance degradation considerin
Externí odkaz:
http://arxiv.org/abs/2409.15077
Accurate depth estimation is crucial for 3D scene comprehension in robotics and autonomous vehicles. Fisheye cameras, known for their wide field of view, have inherent geometric benefits. However, their use in depth estimation is restricted by a scar
Externí odkaz:
http://arxiv.org/abs/2409.15054
We propose depth from coupled optical differentiation, a low-computation passive-lighting 3D sensing mechanism. It is based on our discovery that per-pixel object distance can be rigorously determined by a coupled pair of optical derivatives of a def
Externí odkaz:
http://arxiv.org/abs/2409.10725
We propose PROSE-FD, a zero-shot multimodal PDE foundational model for simultaneous prediction of heterogeneous two-dimensional physical systems related to distinct fluid dynamics settings. These systems include shallow water equations and the Navier
Externí odkaz:
http://arxiv.org/abs/2409.09811
Question recommendation is a task that sequentially recommends questions for students to enhance their learning efficiency. That is, given the learning history and learning target of a student, a question recommender is supposed to select the questio
Externí odkaz:
http://arxiv.org/abs/2409.06177
Autor:
An, Wei, Bi, Xiao, Chen, Guanting, Chen, Shanhuang, Deng, Chengqi, Ding, Honghui, Dong, Kai, Du, Qiushi, Gao, Wenjun, Guan, Kang, Guo, Jianzhong, Guo, Yongqiang, Fu, Zhe, He, Ying, Huang, Panpan, Li, Jiashi, Liang, Wenfeng, Liu, Xiaodong, Liu, Xin, Liu, Yiyuan, Liu, Yuxuan, Lu, Shanghao, Lu, Xuan, Nie, Xiaotao, Pei, Tian, Qiu, Junjie, Qu, Hui, Ren, Zehui, Sha, Zhangli, Su, Xuecheng, Sun, Xiaowen, Tan, Yixuan, Tang, Minghui, Wang, Shiyu, Wang, Yaohui, Wang, Yongji, Xie, Ziwei, Xiong, Yiliang, Xu, Yanhong, Ye, Shengfeng, Yu, Shuiping, Zha, Yukun, Zhang, Liyue, Zhang, Haowei, Zhang, Mingchuan, Zhang, Wentao, Zhang, Yichao, Zhao, Chenggang, Zhao, Yao, Zhou, Shangyan, Zhou, Shunfeng, Zou, Yuheng
The rapid progress in Deep Learning (DL) and Large Language Models (LLMs) has exponentially increased demands of computational power and bandwidth. This, combined with the high costs of faster computing chips and interconnects, has significantly infl
Externí odkaz:
http://arxiv.org/abs/2408.14158