Zobrazeno 1 - 10
of 13 818
pro vyhledávání: '"Kaidi, A."'
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification across multi-lingual and complex co
Externí odkaz:
http://arxiv.org/abs/2411.18162
Autor:
Li, Kun, Zhuang, Shichao, Zhang, Yue, Xu, Minghui, Wang, Ruoxi, Xu, Kaidi, Fu, Xinwen, Cheng, Xiuzhen
Large Language Models (LLMs) excel in diverse tasks such as text generation, data analysis, and software development, making them indispensable across domains like education, business, and creative industries. However, the rapid proliferation of LLMs
Externí odkaz:
http://arxiv.org/abs/2411.10683
It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be
Externí odkaz:
http://arxiv.org/abs/2411.10031
A uniform construction of non-supersymmetric 0-, 4-, 6- and 7-branes in heterotic string theory was announced and outlined in our letter \cite{Kaidi:2023tqo}. In this full paper, we provide details on their properties. Among other things, we discuss
Externí odkaz:
http://arxiv.org/abs/2411.04344
A critical aspect of safe and efficient motion planning for autonomous vehicles (AVs) is to handle the complex and uncertain behavior of surrounding human-driven vehicles (HDVs). Despite intensive research on driver behavior prediction, existing appr
Externí odkaz:
http://arxiv.org/abs/2411.01475
Autor:
Wu, Suhang, Tang, Jialong, Yang, Baosong, Wang, Ante, Jia, Kaidi, Yu, Jiawei, Yao, Junfeng, Su, Jinsong
RALMs (Retrieval-Augmented Language Models) broaden their knowledge scope by incorporating external textual resources. However, the multilingual nature of global knowledge necessitates RALMs to handle diverse languages, a topic that has received limi
Externí odkaz:
http://arxiv.org/abs/2410.21970
Protein structure is key to understanding protein function and is essential for progress in bioengineering, drug discovery, and molecular biology. Recently, with the incorporation of generative AI, the power and accuracy of computational protein stru
Externí odkaz:
http://arxiv.org/abs/2410.20354
For crosslingual conversation and trade, Neural Machine Translation (NMT) is pivotal yet faces persistent challenges with monotony and repetition in generated content. Traditional solutions that rely on penalizing text redundancy or token reoccurrenc
Externí odkaz:
http://arxiv.org/abs/2409.19877
Autor:
Cheng, Hao, Xiao, Erjia, Yu, Chengyuan, Yao, Zhao, Cao, Jiahang, Zhang, Qiang, Wang, Jiaxu, Sun, Mengshu, Xu, Kaidi, Gu, Jindong, Xu, Renjing
Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabulary scenarios for robotic manipulation tasks. Since manipulation tasks
Externí odkaz:
http://arxiv.org/abs/2409.13174