Zobrazeno 1 - 10
of 544
pro vyhledávání: '"Hu, Binbin"'
Autor:
Cai, Guohui, Cai, Ying, Zhang, Zeyu, Ergu, Daji, Cao, Yuanzhouhan, Hu, Binbin, Liao, Zhibin, Zhao, Yang
Pulmonary nodules are critical indicators for the early diagnosis of lung cancer, making their detection essential for timely treatment. However, traditional CT imaging methods suffered from cumbersome procedures, low detection rates, and poor locali
Externí odkaz:
http://arxiv.org/abs/2409.14028
Autor:
Tan, Shengbo, Zhang, Zeyu, Cai, Ying, Ergu, Daji, Wu, Lin, Hu, Binbin, Yu, Pengzhang, Zhao, Yang
Medical imaging segmentation plays a significant role in the automatic recognition and analysis of lesions. State-of-the-art methods, particularly those utilizing transformers, have been prominently adopted in 3D semantic segmentation due to their su
Externí odkaz:
http://arxiv.org/abs/2408.00496
Autor:
Wang, Yakun, Wang, Daixin, Liu, Hongrui, Hu, Binbin, Yan, Yingcui, Zhang, Qiyang, Zhang, Zhiqiang
Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive power of node representation, but recent developments reveal the inferi
Externí odkaz:
http://arxiv.org/abs/2407.20499
Autor:
Wang, Zhe, Zhou, Sheng, Chen, Jiawei, Zhang, Zhen, Hu, Binbin, Feng, Yan, Chen, Chun, Wang, Can
Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement f
Externí odkaz:
http://arxiv.org/abs/2407.16959
Autor:
Wang, Junjie, Chen, Mingyang, Hu, Binbin, Yang, Dan, Liu, Ziqi, Shen, Yue, Wei, Peng, Zhang, Zhiqiang, Gu, Jinjie, Zhou, Jun, Pan, Jeff Z., Zhang, Wen, Chen, Huajun
Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enhance LLMs' performance by combining step-wise planning with external re
Externí odkaz:
http://arxiv.org/abs/2406.14282
Autor:
Gan, Chunjing, Yang, Dan, Hu, Binbin, Zhang, Hanxiao, Li, Siyuan, Liu, Ziqi, Shen, Yue, Ju, Lin, Zhang, Zhiqiang, Gu, Jinjie, Liang, Lei, Zhou, Jun
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucination issues of LLMs have curtailed their applications in knowledge inten
Externí odkaz:
http://arxiv.org/abs/2405.19893
Autor:
Gan, Chunjing, Hu, Binbin, Huang, Bo, Liu, Ziqi, Ma, Jian, Zhang, Zhiqiang, Zhong, Wenliang, Zhou, Jun
Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in. Aiming at effective content delivery, cross-domain reco
Externí odkaz:
http://arxiv.org/abs/2405.17132
Autor:
Zhang, Yichi, Chen, Zhuo, Guo, Lingbing, Xu, Yajing, Hu, Binbin, Liu, Ziqi, Zhang, Wen, Chen, Huajun
Multi-modal knowledge graph completion (MMKGC) aims to automatically discover new knowledge triples in the given multi-modal knowledge graphs (MMKGs), which is achieved by collaborative modeling the structural information concealed in massive triples
Externí odkaz:
http://arxiv.org/abs/2405.16869
Autor:
Zhang, Yichi, Hu, Binbin, Chen, Zhuo, Guo, Lingbing, Liu, Ziqi, Zhang, Zhiqiang, Liang, Lei, Chen, Huajun, Zhang, Wen
Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale KGs and provide unified interfaces to enh
Externí odkaz:
http://arxiv.org/abs/2405.13085
Autor:
Lin, Siyi, Gao, Chongming, Chen, Jiawei, Zhou, Sheng, Hu, Binbin, Feng, Yan, Chen, Chun, Wang, Can
Recommendation Systems (RS) are often plagued by popularity bias. When training a recommendation model on a typically long-tailed dataset, the model tends to not only inherit this bias but often exacerbate it, resulting in over-representation of popu
Externí odkaz:
http://arxiv.org/abs/2404.12008