Zobrazeno 1 - 10
of 18 871
pro vyhledávání: '"ZHU, HONG"'
Autor:
Zhu, Hong-Ming, Pen, Ue-Li
Recent reports of cosmological parity violation in the 4PCF raises the question of how such violations could be systematically generated. Here we present a constructive procedure to generate arbitrary violations of vectorial and tensorial types on an
Externí odkaz:
http://arxiv.org/abs/2409.11400
Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
Autor:
Xi, Yunjia, Liu, Weiwen, Lin, Jianghao, Weng, Muyan, Cai, Xiaoling, Zhu, Hong, Zhu, Jieming, Chen, Bo, Tang, Ruiming, Yu, Yong, Zhang, Weinan
Recommender systems (RSs) play a pervasive role in today's online services, yet their closed-loop nature constrains their access to open-world knowledge. Recently, large language models (LLMs) have shown promise in bridging this gap. However, previou
Externí odkaz:
http://arxiv.org/abs/2408.10520
Many fields could benefit from the rapid development of the large language models (LLMs). The end-to-end autonomous driving (e2eAD) is one of the typically fields facing new opportunities as the LLMs have supported more and more modalities. Here, by
Externí odkaz:
http://arxiv.org/abs/2407.21293
Recent advancements in Connected Vehicle (CV) technology have prompted research on leveraging CV data for more effective traffic management. Despite the low penetration rate, such detailed CV data has demonstrated great potential in improving traffic
Externí odkaz:
http://arxiv.org/abs/2406.14108
With the rapid development of Large Language Models (LLMs), a large number of machine learning models have been developed to assist programming tasks including the generation of program code from natural language input. However, how to evaluate such
Externí odkaz:
http://arxiv.org/abs/2406.12655
In the scenario-based evaluation of machine learning models, a key problem is how to construct test datasets that represent various scenarios. The methodology proposed in this paper is to construct a benchmark and attach metadata to each test case. T
Externí odkaz:
http://arxiv.org/abs/2406.12635
Autor:
Liu, Dugang, Xian, Shenxian, Lin, Xiaolin, Zhang, Xiaolian, Zhu, Hong, Fang, Yuan, Chen, Zhen, Ming, Zhong
The training paradigm integrating large language models (LLM) is gradually reshaping sequential recommender systems (SRS) and has shown promising results. However, most existing LLM-enhanced methods rely on rich textual information on the item side a
Externí odkaz:
http://arxiv.org/abs/2406.00333
Autor:
Jian, Run-Qiang, Zhang, Zhu-Hong
We establish three circles theorems for subharmonic functions on Riemannian manifolds with nonnegative Ricci curvature, as well as on gradient shrinking Ricci solitons with scalar curvature bounded from below by $\frac{n-2}{2}$. We also establish a t
Externí odkaz:
http://arxiv.org/abs/2404.08546
Autor:
Sun, Peijie, Wang, Yifan, Zhang, Min, Wu, Chuhan, Fang, Yan, Zhu, Hong, Fang, Yuan, Wang, Meng
With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. T
Externí odkaz:
http://arxiv.org/abs/2404.08301
This paper addresses the challenge of achieving reliable and robust positioning of a mobile agent, such as a radio device carried by a person, in scenarios where direct line-of-sight (LOS) links are obstructed or unavailable. The human body is consid
Externí odkaz:
http://arxiv.org/abs/2403.16150