Zobrazeno 1 - 10
of 22 180
pro vyhledávání: '"Mengting An"'
Knowledge graphs (KGs), which store an extensive number of relational facts, serve various applications. Recently, personalized knowledge graphs (PKGs) have emerged as a solution to optimize storage costs by customizing their content to align with us
Externí odkaz:
http://arxiv.org/abs/2412.17336
Convolutional neural networks and attention mechanisms have greatly benefited remote sensing change detection (RSCD) because of their outstanding discriminative ability. Existent RSCD methods often follow a paradigm of using a non-interactive Siamese
Externí odkaz:
http://arxiv.org/abs/2412.17247
As one type of blue early-type galaxies, the evolutionary history and fate of star-forming lenticular galaxies (S0s) remain elusive. We selected 134 star-forming S0s from the SDSS-IV MaNGA survey and found that they have steep and warped size-mass re
Externí odkaz:
http://arxiv.org/abs/2412.14517
Autor:
Wang, Jiankang, Xu, Jianjun, Wang, Xiaorui, Wang, Yuxin, Xing, Mengting, Fang, Shancheng, Chen, Zhineng, Xie, Hongtao, Zhang, Yongdong
Synthesizing high-quality reasoning data for continual training has been proven to be effective in enhancing the performance of Large Language Models (LLMs). However, previous synthetic approaches struggle to easily scale up data and incur high costs
Externí odkaz:
http://arxiv.org/abs/2412.08864
Autor:
Wang, Haicheng, Ju, Chen, Lin, Weixiong, Xiao, Shuai, Chen, Mengting, Huang, Yixuan, Liu, Chang, Yao, Mingshuai, Lan, Jinsong, Chen, Ying, Liu, Qingwen, Wang, Yanfeng
In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-training (CLIP) has made significant strides, becoming foundation for various downstream tasks. However, relying on one-to-one (image, text) contrastive paradi
Externí odkaz:
http://arxiv.org/abs/2412.00440
Autor:
Zhang, Zhen, Wang, Xinyu, Jiang, Yong, Chen, Zhuo, Mu, Feiteng, Hu, Mengting, Xie, Pengjun, Huang, Fei
Large Language Models (LLMs) are increasingly recognized for their practical applications. However, these models often encounter challenges in dynamically changing knowledge, as well as in managing unknown static knowledge. Retrieval-Augmented Genera
Externí odkaz:
http://arxiv.org/abs/2411.06207
Autor:
Li, Zhuoshuo, Zhang, Jiong, Zeng, Youbing, Lin, Jiaying, Zhang, Dan, Zhang, Jianjia, Xu, Duan, Kim, Hosung, Liu, Bingguang, Liu, Mengting
Current brain surface-based prediction models often overlook the variability of regional attributes at the cortical feature level. While graph neural networks (GNNs) excel at capturing regional differences, they encounter challenges when dealing with
Externí odkaz:
http://arxiv.org/abs/2411.05825
There is a huge demand to ensure the compliance of smart contracts listed on blockchain platforms to safety and economic standards. Today, manual efforts in the form of auditing are commonly used to achieve this goal. ML-based automated techniques ha
Externí odkaz:
http://arxiv.org/abs/2410.06176
In-vehicle wireless networks are crucial for advancing smart transportation systems and enhancing interaction among vehicles and their occupants. However, there are limited studies in the current state of the art that investigate the in-vehicle chann
Externí odkaz:
http://arxiv.org/abs/2410.02410
Agents, as user-centric tools, are increasingly deployed for human task delegation, assisting with a broad spectrum of requests by generating thoughts, engaging with user proxies, and producing action plans. However, agents based on large language mo
Externí odkaz:
http://arxiv.org/abs/2410.00079