Zobrazeno 1 - 10
of 151
pro vyhledávání: '"Shao, Pengpeng"'
Retrieval-Augmented Generation (RAG) has emerged as a crucial method for addressing hallucinations in large language models (LLMs). While recent research has extended RAG models to complex noisy scenarios, these explorations often confine themselves
Externí odkaz:
http://arxiv.org/abs/2408.13533
Human Action Recognition (HAR) stands as a pivotal research domain in both computer vision and artificial intelligence, with RGB cameras dominating as the preferred tool for investigation and innovation in this field. However, in real-world applicati
Externí odkaz:
http://arxiv.org/abs/2408.09764
Autor:
Chen, Lan, Li, Dong, Wang, Xiao, Shao, Pengpeng, Zhang, Wei, Wang, Yaowei, Tian, Yonghong, Tang, Jin
Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn their features. Although considerable results can be achieved in simple
Externí odkaz:
http://arxiv.org/abs/2406.18845
Autor:
Wu, Jinyang, Che, Feihu, Zheng, Xinxin, Zhang, Shuai, Jin, Ruihan, Nie, Shuai, Shao, Pengpeng, Tao, Jianhua
Large language models (LLMs) like ChatGPT have shown significant advancements across diverse natural language understanding (NLU) tasks, including intelligent dialogue and autonomous agents. Yet, lacking widely acknowledged testing mechanisms, answer
Externí odkaz:
http://arxiv.org/abs/2405.05741
Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using relevant historical facts on the temporal knowledge graphs. There are tw
Externí odkaz:
http://arxiv.org/abs/2204.12036
Autor:
Luo, Jiayu, Shao, Pengpeng, Sun, Zuoxiang, Li, Shuang, Cao, Dandan, Dong, Lijun, Wei, Jianrong, Liu, Jianfeng
Publikováno v:
In Plant Physiology and Biochemistry September 2024 214
Knowledge graph embedding~(KGE) aims to represent entities and relations into low-dimensional vectors for many real-world applications. The representations of entities and relations are learned via contrasting the positive and negative triplets. Thus
Externí odkaz:
http://arxiv.org/abs/2202.09606
Publikováno v:
In Neural Networks July 2024 175
Graph representation learning has attracted a surge of interest recently, whose target at learning discriminant embedding for each node in the graph. Most of these representation methods focus on supervised learning and heavily depend on label inform
Externí odkaz:
http://arxiv.org/abs/2107.02639