Zobrazeno 1 - 10
of 204
pro vyhledávání: '"TANG Keke"'
Autor:
Tang, Keke, Ke, Weiyao, Peng, Weilong, Wang, Xiaofei, Du, Ziyong, Wu, Zhize, Zhu, Peican, Tian, Zhihong
Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Traditional solutions strictly limit point displacement during attacks, making it challenging to balance imperceptibili
Externí odkaz:
http://arxiv.org/abs/2412.19015
Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group ident
Externí odkaz:
http://arxiv.org/abs/2412.18365
Autor:
Fang, Xiang, Fang, Wanlong, Wang, Changshuo, Liu, Daizong, Tang, Keke, Dong, Jianfeng, Zhou, Pan, Li, Beibei
Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous respectable TSG methods have achieved remarkable success, they train
Externí odkaz:
http://arxiv.org/abs/2412.15678
Publikováno v:
Dizhi lixue xuebao, Vol 27, Iss 3, Pp 391-399 (2021)
Intelligent identification of entity relation is an important method and approach to improve literature mining and analysis, and knowledge extraction of gold mine. This study focuses on the core issues affecting current entity relation extraction of
Externí odkaz:
https://doaj.org/article/67656c985f7a4fe2bcda23880d3ba7d0
Graph Neural Network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction and graph classification. The key to the success of GNN lies in its effective structure information representatio
Externí odkaz:
http://arxiv.org/abs/2405.18824
Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. These m
Externí odkaz:
http://arxiv.org/abs/2404.15777
Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is un
Externí odkaz:
http://arxiv.org/abs/2404.15744
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence 2024
Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neural networks, they often require complete user data. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2403.00014
Emerging Large Language Models (LLMs) like GPT-4 have revolutionized Natural Language Processing (NLP), showing potential in traditional tasks such as Named Entity Recognition (NER). Our study explores a three-phase training strategy that harnesses G
Externí odkaz:
http://arxiv.org/abs/2402.09282
Autor:
Tian, Jie, Hou, Jixin, Wu, Zihao, Shu, Peng, Liu, Zhengliang, Xiang, Yujie, Gu, Beikang, Filla, Nicholas, Li, Yiwei, Liu, Ning, Chen, Xianyan, Tang, Keke, Liu, Tianming, Wang, Xianqiao
This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics. Our examination involves a manually crafted
Externí odkaz:
http://arxiv.org/abs/2401.12983