Zobrazeno 1 - 10
of 222
pro vyhledávání: '"Xu, Yucheng"'
World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit
Externí odkaz:
http://arxiv.org/abs/2410.20922
Sparse and noisy images (SNIs), like those in spatial gene expression data, pose significant challenges for effective representation learning and clustering, which are essential for thorough data analysis and interpretation. In response to these chal
Externí odkaz:
http://arxiv.org/abs/2409.01781
Autor:
Elsner, Jan, Xu, Yucheng, Goldberg, Elliot D., Ivanovic, Filip, Dines, Aaron, Giannini, Samuele, Sirringhaus, Henning, Blumberger, Jochen
Thermoelectric materials convert a temperature gradient into a voltage. This phenomenon is relatively well understood for inorganic materials, but much less so for organic semiconductors (OSs). These materials present a challenge because the strong t
Externí odkaz:
http://arxiv.org/abs/2406.18785
Grounding natural language to the physical world is a ubiquitous topic with a wide range of applications in computer vision and robotics. Recently, 2D vision-language models such as CLIP have been widely popularized, due to their impressive capabilit
Externí odkaz:
http://arxiv.org/abs/2406.18742
Autor:
Xu, Yucheng, Yang, Jia-Qi, Fan, Kebin, Wang, Sheng, Wu, Jingbo, Zhang, Caihong, Zhan, De-Chuan, Padilla, Willie J., Jin, Biaobing, Chen, Jian, Wu, Peiheng
Emerging reconfigurable metasurfaces offer various possibilities in programmatically manipulating electromagnetic waves across spatial, spectral, and temporal domains, showcasing great potential for enhancing terahertz applications. However, they are
Externí odkaz:
http://arxiv.org/abs/2405.16795
Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed
Externí odkaz:
http://arxiv.org/abs/2403.16880
Autor:
Tziafas, Georgios, Xu, Yucheng, Goel, Arushi, Kasaei, Mohammadreza, Li, Zhibin, Kasaei, Hamidreza
Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. This work focuses on the task of referring grasp synthesis, which predi
Externí odkaz:
http://arxiv.org/abs/2311.05779
Autor:
Wang, Junda, Yao, Zonghai, Yang, Zhichao, Zhou, Huixue, Li, Rumeng, Wang, Xun, Xu, Yucheng, Yu, Hong
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues. NoteChat embodies the principle that an ensemble of role-specific LLMs, through structured role-play and
Externí odkaz:
http://arxiv.org/abs/2310.15959
Autor:
Mao, Xiaofeng, Xu, Yucheng, Wen, Ruoshi, Kasaei, Mohammadreza, Yu, Wanming, Psomopoulou, Efi, Lepora, Nathan F., Li, Zhibin
Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-efficient learning from demonstration framework which exploits the use of r
Externí odkaz:
http://arxiv.org/abs/2307.04619
Aiding humans with scientific designs is one of the most exciting of artificial intelligence (AI) and machine learning (ML), due to their potential for the discovery of new drugs, design of new materials and chemical compounds, etc. However, scientif
Externí odkaz:
http://arxiv.org/abs/2305.18978