Zobrazeno 1 - 10
of 40 053
pro vyhledávání: '"CHEN, Lei"'
Recent advancements in Large Language Models (LLMs) have significantly enhanced their capacity to process long contexts. However, effectively utilizing this long context remains a challenge due to the issue of distraction, where irrelevant informatio
Externí odkaz:
http://arxiv.org/abs/2411.05928
Large Language Models (LLMs) have recently garnered significant attention in various domains, including recommendation systems. Recent research leverages the capabilities of LLMs to improve the performance and user modeling aspects of recommender sys
Externí odkaz:
http://arxiv.org/abs/2411.02041
Accurate forecasting of spatiotemporal data remains challenging due to complex spatial dependencies and temporal dynamics. The inherent uncertainty and variability in such data often render deterministic models insufficient, prompting a shift towards
Externí odkaz:
http://arxiv.org/abs/2411.01267
Autor:
Chen, Lei, Zhang, Sai, Xu, Fangzhou, Xing, Zhenchang, Wan, Liang, Zhang, Xiaowang, Feng, Zhiyong
In the task of code translation, neural network-based models have been shown to frequently produce semantically erroneous code that deviates from the original logic of the source code. This issue persists even with advanced large models. Although a r
Externí odkaz:
http://arxiv.org/abs/2410.22818
Autor:
Li, Zhuoran, Gao, Zhen, Wang, Kuiyu, Mei, Yikun, Zhu, Chunli, Chen, Lei, Wu, Xiaomei, Niyato, Dusit
To ensure the thriving development of low-altitude economy, countering unauthorized unmanned aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming. In light of
Externí odkaz:
http://arxiv.org/abs/2410.22746
Autor:
Mou, Xinyi, Liang, Jingcong, Lin, Jiayu, Zhang, Xinnong, Liu, Xiawei, Yang, Shiyue, Ye, Rong, Chen, Lei, Kuang, Haoyu, Huang, Xuanjing, Wei, Zhongyu
Large language models (LLMs) are increasingly leveraged to empower autonomous agents to simulate human beings in various fields of behavioral research. However, evaluating their capacity to navigate complex social interactions remains a challenge. Pr
Externí odkaz:
http://arxiv.org/abs/2410.19346
Autor:
Zhou, Xinyi, Li, Xing, Lian, Yingzhao, Wang, Yiwen, Chen, Lei, Yuan, Mingxuan, Hao, Jianye, Chen, Guangyong, Heng, Pheng Ann
We introduce SeaDAG, a semi-autoregressive diffusion model for conditional generation of Directed Acyclic Graphs (DAGs). Considering their inherent layer-wise structure, we simulate layer-wise autoregressive generation by designing different denoisin
Externí odkaz:
http://arxiv.org/abs/2410.16119
Autor:
Wang, Jun, Fang, Meng, Wan, Ziyu, Wen, Muning, Zhu, Jiachen, Liu, Anjie, Gong, Ziqin, Song, Yan, Chen, Lei, Ni, Lionel M., Yang, Linyi, Wen, Ying, Zhang, Weinan
In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (bot
Externí odkaz:
http://arxiv.org/abs/2410.09671
Autor:
Ren, Zheng, Huang, Jianwei, Tan, Hengxin, Biswas, Ananya, Pulkkinen, Aki, Zhang, Yichen, Xie, Yaofeng, Yue, Ziqin, Chen, Lei, Xie, Fang, Allen, Kevin, Wu, Han, Ren, Qirui, Rajapitamahuni, Anil, Kundu, Asish, Vescovo, Elio, Kono, Junichiro, Morosan, Emilia, Dai, Pengcheng, Zhu, Jian-Xin, Si, Qimiao, Minár, Ján, Yan, Binghai, Yi, Ming
Publikováno v:
Nature Communications 15, 9376 (2024)
Magnetic kagome materials provide a fascinating playground for exploring the interplay of magnetism, correlation and topology. Many magnetic kagome systems have been reported including the binary FemXn (X=Sn, Ge; m:n = 3:1, 3:2, 1:1) family and the r
Externí odkaz:
http://arxiv.org/abs/2410.06147
Learned indexes have attracted significant research interest due to their ability to offer better space-time trade-offs compared to traditional B+-tree variants. Among various learned indexes, the PGM-Index based on error-bounded piecewise linear app
Externí odkaz:
http://arxiv.org/abs/2410.00846