Zobrazeno 1 - 10
of 182
pro vyhledávání: '"LI Sihang"'
Autor:
Shi, Yaorui, Li, Sihang, Zhang, Taiyan, Fang, Xi, Wang, Jiankun, Liu, Zhiyuan, Zhao, Guojiang, Zhu, Zhengdan, Gao, Zhifeng, Zhong, Renxin, Zhang, Linfeng, Ke, Guolin, E, Weinan, Cai, Hengxing, Wang, Xiang
Automated drug discovery offers significant potential for accelerating the development of novel therapeutics by substituting labor-intensive human workflows with machine-driven processes. However, a critical bottleneck persists in the inability of cu
Externí odkaz:
http://arxiv.org/abs/2412.07819
Visual localization, which estimates a camera's pose within a known scene, is a long-standing challenge in vision and robotics. Recent end-to-end methods that directly regress camera poses from query images have gained attention for fast inference. H
Externí odkaz:
http://arxiv.org/abs/2412.00138
A proper scene representation is central to the pursuit of spatial intelligence where agents can robustly reconstruct and efficiently understand 3D scenes. A scene representation is either metric, such as landmark maps in 3D reconstruction, 3D boundi
Externí odkaz:
http://arxiv.org/abs/2410.11187
Autor:
Luo, Yanchen, Fang, Junfeng, Li, Sihang, Liu, Zhiyuan, Wu, Jiancan, Zhang, An, Du, Wenjie, Wang, Xiang
The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery. Current generative models are limited to using single property values as conditions, struggling with complex customizations described i
Externí odkaz:
http://arxiv.org/abs/2410.03803
Autor:
Li, Sihang, Huang, Jin, Zhuang, Jiaxi, Shi, Yaorui, Cai, Xiaochen, Xu, Mingjun, Wang, Xiang, Zhang, Linfeng, Ke, Guolin, Cai, Hengxing
Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarkable success of Large Language Models (LLMs), they face challenges in s
Externí odkaz:
http://arxiv.org/abs/2408.15545
Autor:
Liu, Zhiyuan, Shi, Yaorui, Zhang, An, Li, Sihang, Zhang, Enzhi, Wang, Xiang, Kawaguchi, Kenji, Chua, Tat-Seng
Molecule-text modeling, which aims to facilitate molecule-relevant tasks with a textual interface and textual knowledge, is an emerging research direction. Beyond single molecules, studying reaction-text modeling holds promise for helping the synthes
Externí odkaz:
http://arxiv.org/abs/2405.14225
Autor:
Cai, Hengxing, Cai, Xiaochen, Yang, Shuwen, Wang, Jiankun, Yao, Lin, Gao, Zhifeng, Chang, Junhan, Li, Sihang, Xu, Mingjun, Wang, Changxin, Wang, Hongshuai, Li, Yongge, Lin, Mujie, Li, Yaqi, Yin, Yuqi, Zhang, Linfeng, Ke, Guolin
In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, makin
Externí odkaz:
http://arxiv.org/abs/2403.10301
Autor:
Cai, Hengxing, Cai, Xiaochen, Chang, Junhan, Li, Sihang, Yao, Lin, Wang, Changxin, Gao, Zhifeng, Wang, Hongshuai, Li, Yongge, Lin, Mujie, Yang, Shuwen, Wang, Jiankun, Xu, Mingjun, Huang, Jin, Fang, Xi, Zhuang, Jiaxi, Yin, Yuqi, Li, Yaqi, Chen, Changhong, Cheng, Zheng, Zhao, Zifeng, Zhang, Linfeng, Ke, Guolin
Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level
Externí odkaz:
http://arxiv.org/abs/2403.01976
Autor:
Fang, Junfeng, Zhang, Shuai, Wu, Chang, Yang, Zhengyi, Liu, Zhiyuan, Li, Sihang, Wang, Kun, Du, Wenjie, Wang, Xiang
Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research. Recently, the adoption of large language models (LLMs), known for their vast knowledge repositorie
Externí odkaz:
http://arxiv.org/abs/2402.03781
Autor:
Li, Sihang, Liu, Zhiyuan, Luo, Yanchen, Wang, Xiang, He, Xiangnan, Kawaguchi, Kenji, Chua, Tat-Seng, Tian, Qi
Language Models (LMs) have greatly influenced diverse domains. However, their inherent limitation in comprehending 3D molecular structures has considerably constrained their potential in the biomolecular domain. To bridge this gap, we focus on 3D mol
Externí odkaz:
http://arxiv.org/abs/2401.13923