Zobrazeno 1 - 10
of 179
pro vyhledávání: '"Xu, Ruiyang"'
Autor:
Xu, Ruiyang, Cao, Jialun, Lu, Yaojie, Lin, Hongyu, Han, Xianpei, He, Ben, Cheung, Shing-Chi, Sun, Le
Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models' (LLMs) coding capabilities. However, there is an unignorable programming language bias in existing code benchmarks -- over 95% code generation benchmarks are domi
Externí odkaz:
http://arxiv.org/abs/2408.13001
Autor:
Zhu, Zheqing, Braz, Rodrigo de Salvo, Bhandari, Jalaj, Jiang, Daniel, Wan, Yi, Efroni, Yonathan, Wang, Liyuan, Xu, Ruiyang, Guo, Hongbo, Nikulkov, Alex, Korenkevych, Dmytro, Dogan, Urun, Cheng, Frank, Wu, Zheng, Xu, Wanqiao
Publikováno v:
Journal of Machine Learning Research, 2024
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performant RL policy requires a system designed to address several important ch
Externí odkaz:
http://arxiv.org/abs/2312.03814
Autor:
Xu, Ruiyang, Bhandari, Jalaj, Korenkevych, Dmytro, Liu, Fan, He, Yuchen, Nikulkov, Alex, Zhu, Zheqing
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on use
Externí odkaz:
http://arxiv.org/abs/2305.13747
Transformer-based autoregressive (AR) methods have achieved appealing performance for varied sequence-to-sequence generation tasks, e.g., neural machine translation, summarization, and code generation, but suffer from low inference efficiency. To spe
Externí odkaz:
http://arxiv.org/abs/2303.07457
Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly. Existing methods divide features into relevance or irrelevance without
Externí odkaz:
http://arxiv.org/abs/2302.14056
Autor:
Li, Li1 (AUTHOR), Xu, Ruiyang1 (AUTHOR), Wang, Shan1 (AUTHOR), Zhao, Meng1 (AUTHOR), Peng, Sijing2 (AUTHOR), Peng, Xinning1 (AUTHOR), Ye, Qingyuan1 (AUTHOR), Wu, Chen1 (AUTHOR) wuchen@sdu.edu.cn, Wang, Kefang1 (AUTHOR) wangkf@sdu.edu.cn
Publikováno v:
BMC Nursing. 9/2/2024, Vol. 23 Issue 1, p1-9. 9p.
Autor:
Xu, Ruiyang, Chen, Zhengxing
Reinforcement learning (RL) has gained increasing attraction in the academia and tech industry with launches to a variety of impactful applications and products. Although research is being actively conducted on many fronts (e.g., offline RL, performa
Externí odkaz:
http://arxiv.org/abs/2112.05519
Autor:
Xu, Ruiyang, Singh, Ayush
Natural interface to database (NLIDB) has been researched a lot during the past decades. In the core of NLIDB, is a semantic parser used to convert natural language into SQL. Solutions from traditional NLP methodology focuses on grammar rule pattern
Externí odkaz:
http://arxiv.org/abs/2106.13858
Autor:
Zhang, Xiushuo, Wang, Jinjin, Ma, Yu'e, Liu, Dejun, Gao, Ruixin, Xu, Ruiyang, Zhao, Zhibin, Chen, Sheng, Wang, Zhenhai
Publikováno v:
In Engineering Fracture Mechanics 25 March 2024 299
AlphaZero, using a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS), has successfully trained reinforcement learning agents in a tabula-rasa way. The neural MCTS algorithm has been successful in finding near-optimal strategies f
Externí odkaz:
http://arxiv.org/abs/2103.11517