Zobrazeno 1 - 10
of 429
pro vyhledávání: '"Chen Xiaoyin"'
Autor:
Lu, Jiarui, Chen, Xiaoyin, Lu, Stephen Zhewen, Shi, Chence, Guo, Hongyu, Bengio, Yoshua, Tang, Jian
Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling e
Externí odkaz:
http://arxiv.org/abs/2410.18403
Reasoning is a fundamental substrate for solving novel and complex problems. Deliberate efforts in learning and developing frameworks around System 2 reasoning have made great strides, yet problems of sufficient complexity remain largely out of reach
Externí odkaz:
http://arxiv.org/abs/2410.13224
Large language models (LMs) are capable of in-context learning from a few demonstrations (example-label pairs) to solve new tasks during inference. Despite the intuitive importance of high-quality demonstrations, previous work has observed that, in s
Externí odkaz:
http://arxiv.org/abs/2410.09349
Autor:
Lee, Seanie, Seong, Haebin, Lee, Dong Bok, Kang, Minki, Chen, Xiaoyin, Wagner, Dominik, Bengio, Yoshua, Lee, Juho, Hwang, Sung Ju
Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsible deployment of LLMs in real-world applications. However, deploying existing safety guard models with billions
Externí odkaz:
http://arxiv.org/abs/2410.01524
We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise query approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal gr
Externí odkaz:
http://arxiv.org/abs/2402.01207
Autor:
Chen, Xiaoyin, Wiseman, Sam
Given BM25's enduring competitiveness as an information retrieval baseline, we investigate to what extent it can be even further improved by augmenting and re-weighting its sparse query-vector representation. We propose an approach to learning an aug
Externí odkaz:
http://arxiv.org/abs/2305.14087
Autor:
Xing, Bingpeng, Chen, Xiaoyin, Wu, Qiong, Wang, Yanguo, Wang, Chunguang, Xiang, Peng, Sun, Rouxin
Publikováno v:
In Food Control January 2025 167
Autor:
Li, Mengyao a, b, Liu, Dongyu a, Bergen, Phillip J. c, Liang, Silin a, Chen, Juan d, Kho, Zhi Ying c, Lu, Jing e, Sun, Huiying a, Hong, Weiqing a, Liu, Xiaofen f, g, Hong, Chengying a, Chen, Youlian a, Li, Wei a, You, Hongxia h, Xu, Shunyao a, Wang, Yu f, g, Gao, Huaiji i, Lam, Chun Hin j, Li, Jian c, Chen, Xiaoyin k, ⁎⁎, Liu, Xueyan a, ⁎
Publikováno v:
In Heliyon 30 December 2024 10(24)
Autor:
Shen, Zichun a, b, Fang, Wen c, Yu, Zhenxin a, b, Chen, Xiaoyin d, Su, Zhiyu e, Yu, Wen a, b, ⁎, Lin, Heshan d, ⁎⁎
Publikováno v:
In Marine Environmental Research November 2024 202
Autor:
Wang, Siyue, Chen, Xiaoyin, Frederisy, Brent J., Mbakogu, Benedict A., Kanne, Amy D., Khosravi, Pasha, Hayes, Wayne B.
Publikováno v:
"On the current failure--but bright future--of topology-driven biological network alignment". Advances in Protein Chemistry and Structural Biology, Volume 131 (2022), pp. 1-44
The function of a protein is defined by its interaction partners. Thus, topology-driven network alignment of the protein-protein interaction (PPI) networks of two species should uncover similar interaction patterns and allow identification of functio
Externí odkaz:
http://arxiv.org/abs/2204.11999