Zobrazeno 1 - 10
of 11 534
pro vyhledávání: '"Yu, Yong"'
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
Autor:
Xi, Yunjia, Wang, Hangyu, Chen, Bo, Lin, Jianghao, Zhu, Menghui, Liu, Weiwen, Tang, Ruiming, Zhang, Weinan, Yu, Yong
Recently, increasing attention has been paid to LLM-based recommender systems, but their deployment is still under exploration in the industry. Most deployments utilize LLMs as feature enhancers, generating augmentation knowledge in the offline stage
Externí odkaz:
http://arxiv.org/abs/2408.05676
Autor:
Zhu, Jiachen, Lin, Jianghao, Dai, Xinyi, Chen, Bo, Shan, Rong, Zhu, Jieming, Tang, Ruiming, Yu, Yong, Zhang, Weinan
We primarily focus on the field of large language models (LLMs) for recommendation, which has been actively explored recently and poses a significant challenge in effectively enhancing recommender systems with logical reasoning abilities and open-wor
Externí odkaz:
http://arxiv.org/abs/2408.03533
Autor:
Huang, Junjie, Chen, Jizheng, Lin, Jianghao, Qin, Jiarui, Feng, Ziming, Zhang, Weinan, Yu, Yong
In an era dominated by information overload, effective recommender systems are essential for managing the deluge of data across digital platforms. Multi-stage cascade ranking systems are widely used in the industry, with retrieval and ranking being t
Externí odkaz:
http://arxiv.org/abs/2407.21022
Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most existing CRS models mainly focus on dialogue comprehension and preference
Externí odkaz:
http://arxiv.org/abs/2407.04960
In dynamic autonomous driving environment, Artificial Intelligence-Generated Content (AIGC) technology can supplement vehicle perception and decision making by leveraging models' generative and predictive capabilities, and has the potential to enhanc
Externí odkaz:
http://arxiv.org/abs/2407.01956
Autor:
Fu, Lingyue, Guan, Hao, Du, Kounianhua, Lin, Jianghao, Xia, Wei, Zhang, Weinan, Tang, Ruiming, Wang, Yasheng, Yu, Yong
Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational KT scenarios, transductive ID-based methods often face severe data sp
Externí odkaz:
http://arxiv.org/abs/2407.01245