Zobrazeno 1 - 10
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pro vyhledávání: '"Bai, Aijun"'
Autor:
Yue, Zhenrui, Zhuang, Honglei, Bai, Aijun, Hui, Kai, Jagerman, Rolf, Zeng, Hansi, Qin, Zhen, Wang, Dong, Wang, Xuanhui, Bendersky, Michael
The scaling of inference computation has unlocked the potential of long-context large language models (LLMs) across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. Howe
Externí odkaz:
http://arxiv.org/abs/2410.04343
Autor:
Qin, Zhen, Jagerman, Rolf, Pasumarthi, Rama, Zhuang, Honglei, Zhang, He, Bai, Aijun, Hui, Kai, Yan, Le, Wang, Xuanhui
The distillation of ranking models has become an important topic in both academia and industry. In recent years, several advanced methods have been proposed to tackle this problem, often leveraging ranking information from teacher rankers that is abs
Externí odkaz:
http://arxiv.org/abs/2306.04455
Autor:
Bai, Aijun, Jagerman, Rolf, Qin, Zhen, Yan, Le, Kar, Pratyush, Lin, Bing-Rong, Wang, Xuanhui, Bendersky, Michael, Najork, Marc
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that
Externí odkaz:
http://arxiv.org/abs/2211.01494
We summarise the results of RoboCup 2D Soccer Simulation League in 2016 (Leipzig), including the main competition and the evaluation round. The evaluation round held in Leipzig confirmed the strength of RoboCup-2015 champion (WrightEagle, i.e. WE2015
Externí odkaz:
http://arxiv.org/abs/1706.04315
Autor:
Bai, Aijun
The ability for an autonomous agent or robot to track and identify potentially multiple objects in a dynamic environment is essential for many applications, such as automated surveillance, traffic monitoring, human-robot interaction, etc. The main ch
Externí odkaz:
http://arxiv.org/abs/1605.07960
Autor:
Bai, Aijun, Jagerman, Rolf, Qin, Zhen, Kar, Pratyush, Lin, Bing-Rong, Wang, Xuanhui, Bendersky, Michael, Najork, Marc
As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design -- for example, adding a constant to the score of each item on the list will not affect the list ordering. This fun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b615092d1ed8b44f318d44fbb7144049
Akademický článek
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Monte-Carlo tree search is drawing great interest in the domain of planning under uncertainty, particularly when little or no domain knowledge is available. One of the central problems is the trade-off between exploration and exploitation. In this pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______348::e906c24646d0b17d324f5035c817ca36
http://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013
http://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013
Akademický článek
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Publikováno v:
RoboCup 2012: Robot Soccer World Cup XVI; 2013, p141-153, 13p