Zobrazeno 1 - 10
of 9 695
pro vyhledávání: '"Yu, Yong A."'
Wheeled robots have gained significant attention due to their wide range of applications in manufacturing, logistics, and service industries. However, due to the difficulty of building a highly accurate dynamics model for wheeled robots, developing a
Externí odkaz:
http://arxiv.org/abs/2411.09360
Autor:
Weng, Muyan, Xi, Yunjia, Liu, Weiwen, Chen, Bo, Lin, Jianghao, Tang, Ruiming, Zhang, Weinan, Yu, Yong
As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level positive feedback as history (e.g., only clicked items) and ignore that use
Externí odkaz:
http://arxiv.org/abs/2410.20778
Autor:
Zhang, Kangning, Jin, Jiarui, Qin, Yingjie, Su, Ruilong, Lin, Jianghao, Yu, Yong, Zhang, Weinan
Current multimodal recommendation models have extensively explored the effective utilization of multimodal information; however, their reliance on ID embeddings remains a performance bottleneck. Even with the assistance of multimodal information, opt
Externí odkaz:
http://arxiv.org/abs/2410.19276
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly personalized recommendations under strict latency constraints. Multi-s
Externí odkaz:
http://arxiv.org/abs/2410.16080
Autor:
Chen, Liu, Korsch, Alexander Rolf, Kersul, Cauê Moreno, Benevides, Rodrigo, Yu, Yong, Alegre, Thiago P. Mayer, Gröblacher, Simon
Single-photon sources are of fundamental importance to emergent quantum technologies. Nano-structured optomechanical crystals provide an attractive platform for single photon generation due to their unique engineering freedom and compatibility with o
Externí odkaz:
http://arxiv.org/abs/2410.10947
Autor:
Hou, Jinbo, Qiu, Kehai, Zhang, Zitian, Yu, Yong, Wang, Kezhi, Capolongo, Stefano, Zhang, Jiliang, Li, Zeyang, Zhang, Jie
This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) netw
Externí odkaz:
http://arxiv.org/abs/2410.20691
Autor:
Lai, Hang, Cao, Jiahang, Xu, Jiafeng, Wu, Hongtao, Lin, Yunfeng, Kong, Tao, Yu, Yong, Zhang, Weinan
Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision. However, learning directly from high-dimensional visual input is often data-inefficient and
Externí odkaz:
http://arxiv.org/abs/2409.16784
Autor:
Li, Qingyao, Xia, Wei, Du, Kounianhua, Dai, Xinyi, Tang, Ruiming, Wang, Yasheng, Yu, Yong, Zhang, Weinan
LLM agents enhanced by tree search algorithms have yielded notable performances in code generation. However, current search algorithms in this domain suffer from low search quality due to several reasons: 1) Ineffective design of the search space for
Externí odkaz:
http://arxiv.org/abs/2409.09584
Autor:
Lin, Jianghao, Liu, Jiaqi, Zhu, Jiachen, Xi, Yunjia, Liu, Chengkai, Zhang, Yangtian, Yu, Yong, Zhang, Weinan
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inadequate collaborative signals, weak latent representations, and noisy d
Externí odkaz:
http://arxiv.org/abs/2409.05033
Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
Autor:
Xi, Yunjia, Liu, Weiwen, Lin, Jianghao, Weng, Muyan, Cai, Xiaoling, Zhu, Hong, Zhu, Jieming, Chen, Bo, Tang, Ruiming, Yu, Yong, Zhang, Weinan
Recommender systems (RSs) play a pervasive role in today's online services, yet their closed-loop nature constrains their access to open-world knowledge. Recently, large language models (LLMs) have shown promise in bridging this gap. However, previou
Externí odkaz:
http://arxiv.org/abs/2408.10520