Zobrazeno 1 - 10
of 857
pro vyhledávání: '"LI Yinghao"'
Autor:
TIAN Lu, LIU Jinghui, MI Junzhen, ZHAO Baoping, LI Yinghao, ZHANG Sheng, WANG Fengwu, JIAO Weihong, XU Zhenpeng, ZHENG Chengzhong
Publikováno v:
Guan'gai paishui xuebao, Vol 42, Iss 7, Pp 1-9 (2023)
【Objective】 Roots and leaves are highly influenced by a multitude of biotic and abiotic factors. Taking oat under drip irrigation as an example, this study delves into the response of its root and leave traits to planting density and soil amendme
Externí odkaz:
https://doaj.org/article/d06ef9fbd8de49aeacdcb4d8bbb247c6
Large language models demonstrate exceptional performance in simple code generation tasks but still face challenges in tackling complex problems. These challenges may stem from insufficient reasoning and problem decomposition capabilities. To address
Externí odkaz:
http://arxiv.org/abs/2411.11053
Publikováno v:
Jisuanji kexue yu tansuo, Vol 15, Iss 1, Pp 73-83 (2021)
In the era of the Internet of everything, the rapid increase in data volume and computation demand has prompted the evolution of application deployment mode from cloud computing to edge computing in order to reduce bandwidth consumption and response
Externí odkaz:
https://doaj.org/article/1aac65e8a2534d8bae9ad47301410aac
The rapid development of large-scale text-to-speech (TTS) models has led to significant advancements in modeling diverse speaker prosody and voices. However, these models often face issues such as slow inference speeds, reliance on complex pre-traine
Externí odkaz:
http://arxiv.org/abs/2409.10058
The rapid advancement of large language models (LLMs) has significantly propelled the development of text-based chatbots, demonstrating their capability to engage in coherent and contextually relevant dialogues. However, extending these advancements
Externí odkaz:
http://arxiv.org/abs/2408.11849
It is too early to conclude that Mamba is a better alternative to transformers for speech before comparing Mamba with transformers in terms of both performance and efficiency in multiple speech-related tasks. To reach this conclusion, we propose and
Externí odkaz:
http://arxiv.org/abs/2407.09732
Publikováno v:
Journal of International Medical Research, Vol 49 (2021)
Objective To investigate the changes in serum growth hormone (GH), testosterone, and insulin-like growth factor 1 (IGF-1) during low-intensity resistance exercise under different cuff pressures. Methods We performed a single-blind, cross-over design
Externí odkaz:
https://doaj.org/article/f8388153ac524b8b805453219a05c7d2
Domain adaptation aims to enable Large Language Models (LLMs) to generalize domain datasets unseen effectively during the training phase. However, factors such as the size of the model parameters and the scale of training data are general influencers
Externí odkaz:
http://arxiv.org/abs/2406.14828
Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as In-Context Alignment (ICA), indicating that models can comprehend huma
Externí odkaz:
http://arxiv.org/abs/2406.11474
Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER). This paper explores an innovative, cost-efficient str
Externí odkaz:
http://arxiv.org/abs/2403.11103