Zobrazeno 21 - 30
of 2 160
pro vyhledávání: '"Li, Siyuan"'
Autor:
Piccinelli, Luigi, Yang, Yung-Hsu, Sakaridis, Christos, Segu, Mattia, Li, Siyuan, Van Gool, Luc, Yu, Fisher
Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However, the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to
Externí odkaz:
http://arxiv.org/abs/2403.18913
Autor:
Xu, Chao, Liu, Yang, Xing, Jiazheng, Wang, Weida, Sun, Mingze, Dan, Jun, Huang, Tianxin, Li, Siyuan, Cheng, Zhi-Qi, Tai, Ying, Sun, Baigui
In this paper, we abstract the process of people hearing speech, extracting meaningful cues, and creating various dynamically audio-consistent talking faces, termed Listening and Imagining, into the task of high-fidelity diverse talking faces generat
Externí odkaz:
http://arxiv.org/abs/2403.01901
Autor:
Wu, Lirong, Tian, Yijun, Huang, Yufei, Li, Siyuan, Lin, Haitao, Chawla, Nitesh V, Li, Stan Z.
Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play a key role in life activities. The growing demand and cost of experimental PPI assays require computational methods for efficient PPI prediction. While exist
Externí odkaz:
http://arxiv.org/abs/2402.14391
Autor:
Huang, Yufei, Zhang, Odin, Wu, Lirong, Tan, Cheng, Lin, Haitao, Gao, Zhangyang, Li, Siyuan, Li, Stan. Z.
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging. While deep learning has shown promise, existing methods often depend on holo-protein structures (docked, a
Externí odkaz:
http://arxiv.org/abs/2402.11459
Autor:
Li, Siyuan, Liu, Zicheng, Tian, Juanxi, Wang, Ge, Wang, Zedong, Jin, Weiyang, Wu, Di, Tan, Cheng, Lin, Tao, Liu, Yang, Sun, Baigui, Li, Stan Z.
Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DNN) optimization. Despite achieving better flatness, existing WA method
Externí odkaz:
http://arxiv.org/abs/2402.09240
Reinforcement learning (RL) has shown its strength in challenging sequential decision-making problems. The reward function in RL is crucial to the learning performance, as it serves as a measure of the task completion degree. In real-world problems,
Externí odkaz:
http://arxiv.org/abs/2402.07412
Finite-temperature quantum field theory provides the foundation for many important phenomena in the Standard Model and extensions, including phase transitions, baryogenesis, and gravitational waves. Methods are developed to enable application of pySe
Externí odkaz:
http://arxiv.org/abs/2401.05258
Autor:
Li, Siyuan, Zhang, Luyuan, Wang, Zedong, Wu, Di, Wu, Lirong, Liu, Zicheng, Xia, Jun, Tan, Cheng, Liu, Yang, Sun, Baigui, Li, Stan Z.
As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and the low dependence on labeled data. Among these varied self-supervised
Externí odkaz:
http://arxiv.org/abs/2401.00897
Autor:
Zheng, Jiangbin, Li, Siyuan, Huang, Yufei, Gao, Zhangyang, Tan, Cheng, Hu, Bozhen, Xia, Jun, Wang, Ge, Li, Stan Z.
Protein design involves generating protein sequences based on their corresponding protein backbones. While deep generative models show promise for learning protein design directly from data, the lack of publicly available structure-sequence pairings
Externí odkaz:
http://arxiv.org/abs/2312.06297
Autor:
Tan, Cheng, Wei, Jingxuan, Gao, Zhangyang, Sun, Linzhuang, Li, Siyuan, Guo, Ruifeng, Yu, Bihui, Li, Stan Z.
Multimodal reasoning is a challenging task that requires models to reason across multiple modalities to answer questions. Existing approaches have made progress by incorporating language and visual modalities into a two-stage reasoning framework, sep
Externí odkaz:
http://arxiv.org/abs/2311.14109