Zobrazeno 1 - 10
of 327
pro vyhledávání: '"GUO Zhicheng"'
Publikováno v:
Heliyon, Vol 10, Iss 15, Pp e35145- (2024)
Core strength training plays an essential role in maximizing performance for badminton athletes. The core muscles in the abdominal, back, and hip regions provide stability, enable efficient power transfer between the upper and lower body, and allow f
Externí odkaz:
https://doaj.org/article/f4e3e80857ed40e58886590e4f4a5aff
Publikováno v:
发电技术, Vol 43, Iss 2, Pp 353-361 (2022)
Based on the tunable diode laser absorption spectroscopy (TDLAS) technology, one-dimensional and multi-dimensional combustion parameter monitoring systems were designed to simultaneously monitor multiple combustion field parameters in the furnace (su
Externí odkaz:
https://doaj.org/article/60287d8350f94d3d85cf123b6dd62d3f
Autor:
Yu, Yuanqing, Wang, Zhefan, Ma, Weizhi, Guo, Zhicheng, Zhan, Jingtao, Wang, Shuai, Wu, Chuhan, Guo, Zhiqiang, Zhang, Min
Despite having powerful reasoning and inference capabilities, Large Language Models (LLMs) still need external tools to acquire real-time information retrieval or domain-specific expertise to solve complex tasks, which is referred to as tool learning
Externí odkaz:
http://arxiv.org/abs/2410.07745
Multilingual large language models are designed, claimed, and expected to cater to speakers of varied languages. We hypothesise that the current practices of fine-tuning and evaluating these models may not perfectly align with this objective owing to
Externí odkaz:
http://arxiv.org/abs/2406.12822
Foundation models, especially those using transformers as backbones, have gained significant popularity, particularly in language and language-vision tasks. However, large foundation models are typically trained on high-quality data, which poses a si
Externí odkaz:
http://arxiv.org/abs/2404.17667
Autor:
Guo, Zhicheng, Cheng, Sijie, Wang, Hao, Liang, Shihao, Qin, Yujia, Li, Peng, Liu, Zhiyuan, Sun, Maosong, Liu, Yang
Large Language Models (LLMs) have witnessed remarkable advancements in recent years, prompting the exploration of tool learning, which integrates LLMs with external tools to address diverse real-world challenges. Assessing the capability of LLMs to u
Externí odkaz:
http://arxiv.org/abs/2403.07714
The performance of machine learning models heavily depends on the quality of input data, yet real-world applications often encounter various data-related challenges. One such challenge could arise when curating training data or deploying the model in
Externí odkaz:
http://arxiv.org/abs/2403.05652
Autor:
Yang, Zonghan, Liu, An, Liu, Zijun, Liu, Kaiming, Xiong, Fangzhou, Wang, Yile, Yang, Zeyuan, Hu, Qingyuan, Chen, Xinrui, Zhang, Zhenhe, Luo, Fuwen, Guo, Zhicheng, Li, Peng, Liu, Yang
The rapid progress of foundation models has led to the prosperity of autonomous agents, which leverage the universal capabilities of foundation models to conduct reasoning, decision-making, and environmental interaction. However, the efficacy of agen
Externí odkaz:
http://arxiv.org/abs/2402.07744
This paper explores the challenges in evaluating machine learning (ML) models for continuous health monitoring using wearable devices beyond conventional metrics. We state the complexities posed by real-world variability, disease dynamics, user-speci
Externí odkaz:
http://arxiv.org/abs/2312.02300
Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities, with the majority focusing on the third-person perspective, and only a few addressing spec
Externí odkaz:
http://arxiv.org/abs/2311.15596