Zobrazeno 1 - 10
of 1 088
pro vyhledávání: '"Yang Yanqing"'
GDP is a vital measure of a country's economic health, reflecting the total value of goods and services produced. Forecasting GDP growth is essential for economic planning, as it helps governments, businesses, and investors anticipate trends, make in
Externí odkaz:
http://arxiv.org/abs/2409.02551
Large language models (LLMs) have demonstrated impressive versatility across numerous tasks, yet their generalization capabilities remain poorly understood. To investigate these behaviors, arithmetic tasks serve as important venues. In previous studi
Externí odkaz:
http://arxiv.org/abs/2407.17963
We propose a deep learning framework, DL-opt, designed to efficiently solve for optimal policies in quantifiable general equilibrium trade models. DL-opt integrates (i) a nested fixed point (NFXP) formulation of the optimization problem, (ii) automat
Externí odkaz:
http://arxiv.org/abs/2407.17731
Autor:
Yu, Yi, Yu, Jingru, Wang, Xuhong, Li, Juanjuan, Lin, Yilun, He, Conghui, Yang, Yanqing, Qiao, Yu, Li, Li, Wang, Fei-Yue
Data has been increasingly recognized as a critical factor in the future economy. However, constructing an efficient data trading market faces challenges such as privacy breaches, data monopolies, and misuse. Despite numerous studies proposing algori
Externí odkaz:
http://arxiv.org/abs/2407.11466
This paper aims to explore the application of machine learning in forecasting Chinese macroeconomic variables. Specifically, it employs various machine learning models to predict the quarterly real GDP growth of China, and analyzes the factors contri
Externí odkaz:
http://arxiv.org/abs/2407.03595
This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following Eloundou et al. (2023)'s m
Externí odkaz:
http://arxiv.org/abs/2308.08776
Publikováno v:
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
Large language models (LLMs) have achieved remarkable proficiency on solving diverse problems. However, their generalization ability is not always satisfying and the generalization problem is common for generative transformer models in general. Resea
Externí odkaz:
http://arxiv.org/abs/2308.08268
Identification of a covalent NEK7 inhibitor to alleviate NLRP3 inflammasome-driven metainflammation.
Autor:
Jin, Xiangyu1 (AUTHOR), Yang, Yanqing2 (AUTHOR), Liu, Didi3 (AUTHOR), Zhou, Xinru3 (AUTHOR), Huang, Yi1 (AUTHOR) HY527@ihm.ac.cn
Publikováno v:
Cell Communication & Signaling. 11/25/2024, Vol. 22 Issue 1, p1-13. 13p.
Publikováno v:
In Nano TransMed December 2024 3
Autor:
Yang, Yanqing a, 1, Li, Ao a, 1, Guo, Mingmei a, Kong, Yanhui b, Zhang, Juanyue a, Wang, Jingyue a, Sun, Shuyang a, Li, Xiulian c, Zeng, Xiangquan d, Gong, Hansheng a, ⁎, Fan, Xinguang a, ⁎
Publikováno v:
In Scientia Horticulturae 1 December 2024 338