Zobrazeno 1 - 10
of 795
pro vyhledávání: '"Wang Minjie"'
Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (LLMs) have shown promise in automating various data science tasks, inc
Externí odkaz:
http://arxiv.org/abs/2410.12865
Heterogeneous Graph Neural Networks (HGNNs) leverage diverse semantic relationships in Heterogeneous Graphs (HetGs) and have demonstrated remarkable learning performance in various applications. However, current distributed GNN training systems often
Externí odkaz:
http://arxiv.org/abs/2408.09697
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems,2024
The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly op
Externí odkaz:
http://arxiv.org/abs/2408.01248
Autor:
Liu, Renjie, Wang, Yichuan, Yan, Xiao, Cai, Zhenkun, Wang, Minjie, Jiang, Haitian, Tang, Bo, Li, Jinyang
Graph neural networks (GNNs) are machine learning models specialized for graph data and widely used in many applications. To train GNNs on large graphs that exceed CPU memory, several systems store data on disk and conduct out-of-core processing. How
Externí odkaz:
http://arxiv.org/abs/2405.05231
Autor:
Wang, Minjie, Gan, Quan, Wipf, David, Cai, Zhenkun, Li, Ning, Tang, Jianheng, Zhang, Yanlin, Zhang, Zizhao, Mao, Zunyao, Song, Yakun, Wang, Yanbo, Li, Jiahang, Zhang, Han, Yang, Guang, Qin, Xiao, Lei, Chuan, Zhang, Muhan, Zhang, Weinan, Faloutsos, Christos, Zhang, Zheng
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision o
Externí odkaz:
http://arxiv.org/abs/2404.18209
Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy function serves as input features to an upper-level objective of interest. These optimal features typically depend on tunable parameters of the lower-level
Externí odkaz:
http://arxiv.org/abs/2403.04763
Entanglement swapping (ES) between memory repeater links is critical for establishing quantum networks via quantum repeaters. So far, ES with atomic-ensemble-based memories has not been achieved. Here, we experimentally demonstrated ES between two en
Externí odkaz:
http://arxiv.org/abs/2401.00519
This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmea
Externí odkaz:
http://arxiv.org/abs/2310.16698
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy funct
Externí odkaz:
http://arxiv.org/abs/2310.12457
Autor:
Wang, Minjie, Jiao, Haole, Lu, Jiajin, Fan, Wenxin, Yang, Zhifang, Xi, Mengqi, Li, Shujing, Wang, Hai
Duan-Lukin-Cirac-Zoller quantum repeater protocol provides a feasible scheme to implement long-distance quantum communication and large-scale quantum networks. The elementary link, namely the entanglement between two atomic ensembles, is a fundamenta
Externí odkaz:
http://arxiv.org/abs/2308.14587