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pro vyhledávání: '"Wu,Nan"'
Exploring the predictive capabilities of language models in material science is an ongoing interest. This study investigates the application of language model embeddings to enhance material property prediction in materials science. By evaluating vari
Externí odkaz:
http://arxiv.org/abs/2410.16165
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
Addressing the incompleteness problem in knowledge graph remains a significant challenge. Current knowledge graph completion methods have their limitations. For example, ComDensE is prone to overfitting and suffers from the degradation with the incre
Externí odkaz:
http://arxiv.org/abs/2410.07140
Autor:
Wu, Nan, Lin, Jing, Xie, Changrong, Guo, Zechen, Huang, Wenhui, Zhang, Libo, Zhou, Yuxuan, Sun, Xuandong, Zhang, Jiawei, Guo, Weijie, Linpeng, Xiayu, Liu, Song, Liu, Yang, Ren, Wenhui, Tao, Ziyu, Jiang, Ji, Chu, Ji, Niu, Jingjing, Zhong, Youpeng, Yu, Dapeng
Mixers play a crucial role in superconducting quantum computing, primarily by facilitating frequency conversion of signals to enable precise control and readout of quantum states. However, imperfections, particularly carrier leakage and unwanted side
Externí odkaz:
http://arxiv.org/abs/2408.11671
Prognosis prediction is crucial for determining optimal treatment plans for lung cancer patients. Traditionally, such predictions relied on models developed from retrospective patient data. Recently, large language models (LLMs) have gained attention
Externí odkaz:
http://arxiv.org/abs/2408.07971
Lymph node metastasis (LNM) is a crucial factor in determining the initial treatment for patients with lung cancer, yet accurate preoperative diagnosis of LNM remains challenging. Recently, large language models (LLMs) have garnered significant atten
Externí odkaz:
http://arxiv.org/abs/2407.17900
We study how the posterior contraction rate under a Gaussian process (GP) prior depends on the intrinsic dimension of the predictors and smoothness of the regression function. An open question is whether a generic GP prior that does not incorporate k
Externí odkaz:
http://arxiv.org/abs/2407.09286
Autor:
Kuo, Pei-Cheng, Wu, Nan
In the study of high-dimensional data, it is often assumed that the data set possesses an underlying lower-dimensional structure. A practical model for this structure is an embedded compact manifold with boundary. Since the underlying manifold struct
Externí odkaz:
http://arxiv.org/abs/2406.18456
Autor:
Sabir, Bushra, Yang, Shuiqiao, Nguyen, David, Wu, Nan, Abuadbba, Alsharif, Suzuki, Hajime, Lai, Shangqi, Ni, Wei, Ming, Ding, Nepal, Surya
Artificial Intelligence (AI) has advanced significantly in various domains like healthcare, finance, and cybersecurity, with successes such as DeepMind's medical imaging and Tesla's autonomous vehicles. As telecommunications transition from 5G to 6G,
Externí odkaz:
http://arxiv.org/abs/2407.10981
Due to the widespread adoption of "work-from-home" policies, videoconferencing applications (e.g., Zoom) have become indispensable for remote communication. However, they often lack immersiveness, leading to the so-called "Zoom fatigue" and degrading
Externí odkaz:
http://arxiv.org/abs/2405.10422