Zobrazeno 1 - 10
of 9 249
pro vyhledávání: '"Yu, Guang"'
Autor:
Shen, Yiqing, Chen, Zan, Mamalakis, Michail, Liu, Yungeng, Li, Tianbin, Su, Yanzhou, He, Junjun, Liò, Pietro, Wang, Yu Guang
The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains. While large language models (LLMs) have achieved much progress in the domain of natural language proce
Externí odkaz:
http://arxiv.org/abs/2408.15299
Autor:
Li, Yang, Cai, Wen-Qi, Ren, Ji-Gang, Wang, Chao-Ze, Yang, Meng, Zhang, Liang, Wu, Hui-Ying, Chang, Liang, Wu, Jin-Cai, Jin, Biao, Xue, Hua-Jian, Li, Xue-Jiao, Liu, Hui, Yu, Guang-Wen, Tao, Xue-Ying, Chen, Ting, Liu, Chong-Fei, Luo, Wen-Bin, Zhou, Jie, Yong, Hai-Lin, Li, Yu-Huai, Li, Feng-Zhi, Jiang, Cong, Chen, Hao-Ze, Wu, Chao, Tong, Xin-Hai, Xie, Si-Jiang, Zhou, Fei, Liu, Wei-Yue, Liu, Nai-Le, Li, Li, Xu, Feihu, Cao, Yuan, Yin, Juan, Shu, Rong, Wang, Xiang-Bin, Zhang, Qiang, Wang, Jian-Yu, Liao, Sheng-Kai, Peng, Cheng-Zhi, Pan, Jian-Wei
A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guara
Externí odkaz:
http://arxiv.org/abs/2408.10994
Graph neural networks (GNNs) have become pivotal tools for processing graph-structured data, leveraging the message passing scheme as their core mechanism. However, traditional GNNs often grapple with issues such as instability, over-smoothing, and o
Externí odkaz:
http://arxiv.org/abs/2407.06988
Autor:
Kulytė, Paulina, Vargas, Francisco, Mathis, Simon Valentin, Wang, Yu Guang, Hernández-Lobato, José Miguel, Liò, Pietro
Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs determines the antibody's affinity and specificity towards its target. Gen
Externí odkaz:
http://arxiv.org/abs/2406.05832
Autor:
Shen, Yiqing, Chen, Zan, Mamalakis, Michail, He, Luhan, Xia, Haiyang, Li, Tianbin, Su, Yanzhou, He, Junjun, Wang, Yu Guang
The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding. Despite the success of LLMs in NLP, their effectiveness in comprehendi
Externí odkaz:
http://arxiv.org/abs/2406.05540
Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert prohibitiv
Externí odkaz:
http://arxiv.org/abs/2405.12474
Large language models (LLMs) have garnered considerable attention for their proficiency in tackling intricate tasks, particularly leveraging their capacities for zero-shot and in-context learning. However, their utility has been predominantly restric
Externí odkaz:
http://arxiv.org/abs/2405.06658
Graph neural networks (GNNs) are a powerful solution for various structure learning applications due to their strong representation capabilities for graph data. However, traditional GNNs, relying on message-passing mechanisms that gather information
Externí odkaz:
http://arxiv.org/abs/2403.11408
Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smo
Externí odkaz:
http://arxiv.org/abs/2311.05767
Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protein backbone. This task involves not only identifying viable sequences b
Externí odkaz:
http://arxiv.org/abs/2306.16819