Zobrazeno 1 - 10
of 81 491
pro vyhledávání: '"Kam, A."'
The realization of fault-tolerant quantum computers hinges on effective quantum error correction protocols, whose performance significantly relies on the nature of the underlying noise. In this work, we directly study the structure of non-Markovian c
Externí odkaz:
http://arxiv.org/abs/2410.23779
Autor:
Tong, Kam Hung
It is a classical result that the set $K\backslash G /B$ is finite, where $G$ is a reductive algebraic group over an algebraically closed field with characteristic not equal to two, $B$ is a Borel subgroup of $G$, and $K = G^{\theta}$ is the fixed po
Externí odkaz:
http://arxiv.org/abs/2410.19442
Autor:
Wang, Zezhong, Zeng, Xingshan, Liu, Weiwen, Li, Liangyou, Wang, Yasheng, Shang, Lifeng, Jiang, Xin, Liu, Qun, Wong, Kam-Fai
Supervised fine-tuning (SFT) is a common method to enhance the tool calling capabilities of Large Language Models (LLMs), with the training data often being synthesized. The current data synthesis process generally involves sampling a set of tools, f
Externí odkaz:
http://arxiv.org/abs/2410.18447
Autor:
Zhao, Yu, Du, Xiaotang, Hong, Giwon, Gema, Aryo Pradipta, Devoto, Alessio, Wang, Hongru, He, Xuanli, Wong, Kam-Fai, Minervini, Pasquale
Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context. Such conflicts can lead to undesirable model behaviou
Externí odkaz:
http://arxiv.org/abs/2410.16090
Autor:
Zhao, Yu, Devoto, Alessio, Hong, Giwon, Du, Xiaotang, Gema, Aryo Pradipta, Wang, Hongru, He, Xuanli, Wong, Kam-Fai, Minervini, Pasquale
Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context -- this phenomenon, known as \emph{context-memory know
Externí odkaz:
http://arxiv.org/abs/2410.15999
Autor:
Xue, Boyang, Wang, Hongru, Wang, Rui, Wang, Sheng, Wang, Zezhong, Du, Yiming, Liang, Bin, Wong, Kam-Fai
The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trustworthiness of the generations become essential. However, current LLM
Externí odkaz:
http://arxiv.org/abs/2410.12478
In Federated Learning (FL), anomaly detection (AD) is a challenging task due to the decentralized nature of data and the presence of non-IID data distributions. This study introduces a novel federated threshold calculation method that leverages summa
Externí odkaz:
http://arxiv.org/abs/2410.09284
Autor:
Wang, Hongru, Wang, Rui, Xue, Boyang, Xia, Heming, Cao, Jingtao, Liu, Zeming, Pan, Jeff Z., Wong, Kam-Fai
Large Language Models (LLMs) can interact with the real world by connecting with versatile external APIs, resulting in better problem-solving and task automation capabilities. Previous research primarily focuses on APIs with limited arguments from a
Externí odkaz:
http://arxiv.org/abs/2410.19743
Autor:
Haworth, Jesse, Biswas, Rishi, Opfermann, Justin, Kam, Michael, Wang, Yaning, Pantalone, Desire, Creighton, Francis X., Yang, Robin, Kang, Jin U., Krieger, Axel
Vascular anastomosis, the surgical connection of blood vessels, is essential in procedures such as organ transplants and reconstructive surgeries. The precision required limits accessibility due to the extensive training needed, with manual suturing
Externí odkaz:
http://arxiv.org/abs/2410.07493
Autor:
Kiendrebeogo, R. Weizmann, Saleem, Muhammed, Bizouard, Marie Anne, Christensen, Nelson, Coughlin, Michael W., Janssens, Kamiel, Kam, S. Zacharie, Koulidiati, Jean
Since the first detection of gravitational waves (GW) in 2015, there have been significant efforts to enhance the sensitivity of the ground-based detectors within the LIGO-Virgo-KAGRA collaboration. Despite these efforts, many gravitational wave sign
Externí odkaz:
http://arxiv.org/abs/2410.06220