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pro vyhledávání: '"Zhu, Ronghang"'
Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness an
Externí odkaz:
http://arxiv.org/abs/2410.07589
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these systems. To make AI systems goo
Externí odkaz:
http://arxiv.org/abs/2308.12315
Publikováno v:
In Computers and Electronics in Agriculture October 2024 225
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classifi
Externí odkaz:
http://arxiv.org/abs/2107.04713
Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of ma
Externí odkaz:
http://arxiv.org/abs/2010.13242
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This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recogn
Externí odkaz:
http://arxiv.org/abs/1803.11366
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Aug2024, Vol. 18 Issue 7, p1-53, 53p
Akademický článek
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Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; August 2023, Vol. 34 Issue: 8 p3847-3858, 12p