Zobrazeno 1 - 10
of 7 629
pro vyhledávání: '"Zeng, Li"'
Autor:
Cheng, Zhixiang, Xiang, Hongxin, Ma, Pengsen, Zeng, Li, Jin, Xin, Yang, Xixi, Lin, Jianxin, Deng, Yang, Song, Bosheng, Feng, Xinxin, Deng, Changhui, Zeng, Xiangxiang
Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them. Our research indicates t
Externí odkaz:
http://arxiv.org/abs/2409.12926
Autor:
Cai, Xiaoxuan, Zeng, Li, Fowler, Charlotte, Dixon, Lisa, Ongur, Dost, Baker, Justin T., Onnela, Jukka-Pekka, Valeri, Linda
Mobile technology (mobile phones and wearable devices) generates continuous data streams encompassing outcomes, exposures and covariates, presented as intensive longitudinal or multivariate time series data. The high frequency of measurements enables
Externí odkaz:
http://arxiv.org/abs/2407.17666
Attosecond x-ray pulses play a crucial role in the study of ultrafast phenomena occurring within inner and valence electrons. Especially isolated attosecond pulses with high photon energy and high peak power are of great significance in single-shot i
Externí odkaz:
http://arxiv.org/abs/2406.14843
Autor:
Li, Jing, Sun, Zhijie, Lin, Dachao, He, Xuan, Lin, Yi, Zheng, Binfan, Zeng, Li, Zhao, Rongqian, Chen, Xin
Mixture-of-Experts (MoE) architectures have emerged as a paradigm-shifting approach for large language models (LLMs), offering unprecedented computational efficiency. However, these architectures grapple with challenges of token distribution imbalanc
Externí odkaz:
http://arxiv.org/abs/2406.00023
In this paper, we extend financial sentiment analysis~(FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictio
Externí odkaz:
http://arxiv.org/abs/2404.08681
Autor:
Pu, Tianle, Fan, Changjun, Shen, Mutian, Lu, Yizhou, Zeng, Li, Nussinov, Zohar, Chen, Chao, Liu, Zhong
Many complex problems encountered in both production and daily life can be conceptualized as combinatorial optimization problems (COPs) over graphs. Recent years, reinforcement learning (RL) based models have emerged as a promising direction, which t
Externí odkaz:
http://arxiv.org/abs/2404.04661
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. F
Externí odkaz:
http://arxiv.org/abs/2404.01875
Autor:
Zeng, Li, Huang, Haohan, Zheng, Binfan, Yang, Kang, Shao, Shengcheng, Zhou, Jinhua, Xie, Jun, Zhao, Rongqian, Chen, Xin
Graph Partitioning is widely used in many real-world applications such as fraud detection and social network analysis, in order to enable the distributed graph computing on large graphs. However, existing works fail to balance the computation cost an
Externí odkaz:
http://arxiv.org/abs/2403.00331
Autor:
Li, Jing, Sun, Zhijie, He, Xuan, Zeng, Li, Lin, Yi, Li, Entong, Zheng, Binfan, Zhao, Rongqian, Chen, Xin
The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited
Externí odkaz:
http://arxiv.org/abs/2401.13920
Autor:
Gao, Yu, Qin, Meng, Ding, Yibin, Zeng, Li, Zhang, Chaorui, Zhang, Weixi, Han, Wei, Zhao, Rongqian, Bai, Bo
Graph partitioning (GP), a.k.a. community detection, is a classic problem that divides the node set of a graph into densely-connected blocks. Following prior work on the IEEE HPEC Graph Challenge benchmark and recent advances in graph machine learnin
Externí odkaz:
http://arxiv.org/abs/2312.01560