Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Zhao, Mengchen"'
Autor:
Fu, Zichuan, Li, Xiangyang, Wu, Chuhan, Wang, Yichao, Dong, Kuicai, Zhao, Xiangyu, Zhao, Mengchen, Guo, Huifeng, Tang, Ruiming
Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them. Given the availability of various services like on
Externí odkaz:
http://arxiv.org/abs/2312.10743
Autor:
Yang, Sicheng, Wu, Zhiyong, Li, Minglei, Zhao, Mengchen, Lin, Jiuxin, Chen, Liyang, Bao, Weihong
This paper describes the ReprGesture entry to the Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022. The GENEA challenge provides the processed datasets and performs crowdsourced evaluations to compare the p
Externí odkaz:
http://arxiv.org/abs/2208.12133
In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with environment. In multi-player Markov games (MGs), however, the interaction is non-stationary due to the behaviors of other players, so the agent
Externí odkaz:
http://arxiv.org/abs/2110.08979
Practical recommender systems experience a cold-start problem when observed user-item interactions in the history are insufficient. Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of
Externí odkaz:
http://arxiv.org/abs/2108.10511
Autor:
Chen, Yankai, Yang, Menglin, Zhang, Yingxue, Zhao, Mengchen, Meng, Ziqiao, Hao, Jianye, King, Irwin
Aiming to alleviate data sparsity and cold-start problems of traditional recommender systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has recently gained considerable attention. Via unifying the KG with user-item inte
Externí odkaz:
http://arxiv.org/abs/2108.06468
Autor:
Zhao, Mengchen1 (AUTHOR) sgomezro@uwo.ca, Gomez-Rosero, Santiago1 (AUTHOR) mcapretz@uwo.ca, Nouraei, Hooman2 (AUTHOR), Zych, Craig2 (AUTHOR), Capretz, Miriam A. M.1 (AUTHOR), Sadhu, Ayan3 (AUTHOR) asadhu@uwo.ca
Publikováno v:
Energies (19961073). Apr2024, Vol. 17 Issue 7, p1672. 24p.
Communicating with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer from restrictions on limited-bandwidth communication. If the bandwidth i
Externí odkaz:
http://arxiv.org/abs/2010.04978
Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, the fixed rollout with an inaccurate model has a potential to harm the learning proces
Externí odkaz:
http://arxiv.org/abs/2009.09593
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism. Deploying a number of (video) cameras is a common surveillance approach. Given the never-ending power offered by vehicles to metropolises, e
Externí odkaz:
http://arxiv.org/abs/1705.08508