Zobrazeno 1 - 10
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pro vyhledávání: '"LI PAN"'
Autor:
Li, Pan
In this paper, we investigate the Hilbert space factorisation problem of two-sided black holes in high dimensions. We demonstrate that the Hilbert space of two-sided black holes can be factorized into the tensor product of two one-sided bulk Hilbert
Externí odkaz:
http://arxiv.org/abs/2410.23886
Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in
Externí odkaz:
http://arxiv.org/abs/2410.20724
Autor:
Zhang, Kexin, Liu, Shuhan, Wang, Song, Shi, Weili, Chen, Chen, Li, Pan, Li, Sheng, Li, Jundong, Ding, Kaize
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in real-world scenarios. These shifts may severely deteriorate model perform
Externí odkaz:
http://arxiv.org/abs/2410.19265
Graph neural networks (GNNs) have achieved remarkable success in a variety of machine learning tasks over graph data. Existing GNNs usually rely on message passing, i.e., computing node representations by gathering information from the neighborhood,
Externí odkaz:
http://arxiv.org/abs/2410.09737
Graphs offer unique insights into relationships and interactions between entities, complementing data modalities like text, images, and videos. By incorporating relational information from graph data, AI models can extend their capabilities beyond tr
Externí odkaz:
http://arxiv.org/abs/2410.08299
To keep pace with the rapid advancements in design complexity within modern computing systems, directed graph representation learning (DGRL) has become crucial, particularly for encoding circuit netlists, computational graphs, and developing surrogat
Externí odkaz:
http://arxiv.org/abs/2410.06460
Autor:
Chien, Eli, Li, Pan
We study the Differential Privacy (DP) guarantee of hidden-state Noisy-SGD algorithms over a bounded domain. Standard privacy analysis for Noisy-SGD assumes all internal states are revealed, which leads to a divergent R'enyi DP bound with respect to
Externí odkaz:
http://arxiv.org/abs/2410.01068
Autor:
He, Wen-Jie, Zhang, Ren-You, Han, Liang, Jiang, Yi, Li, Zhe, Wang, Xiao-Feng, Li, Shu-Xiang, Li, Pan-Feng, Wang, Qing-hai
The planar two-loop scalar Feynman integrals contributing to the massive NNLO QCD corrections for $W$-boson pair production via quark-antiquark annihilation can be classified into three family branches, each of which is reduced to a distinct set of m
Externí odkaz:
http://arxiv.org/abs/2409.08879
Autor:
Guo, Tiezheng, Wang, Chen, Liu, Yanyi, Tang, Jiawei, Li, Pan, Xu, Sai, Yang, Qingwen, Gao, Xianlin, Li, Zhi, Wen, Yingyou
Retrieving external knowledge and prompting large language models with relevant information is an effective paradigm to enhance the performance of question-answering tasks. Previous research typically handles paragraphs from external documents in iso
Externí odkaz:
http://arxiv.org/abs/2408.02907
Positional encodings (PEs) are essential for building powerful and expressive graph neural networks and graph transformers, as they effectively capture the relative spatial relationships between nodes. Although extensive research has been devoted to
Externí odkaz:
http://arxiv.org/abs/2407.20912