no code implementations • 28 Apr 2024 • Zilong Bai, ruiji zhang, Linqing Chen, Qijun Cai, Yuan Zhong, Cong Wang, Yan Fang, Jie Fang, Jing Sun, Weikuan Wang, Lizhi Zhou, Haoran Hua, Tian Qiu, Chaochao Wang, Cheng Sun, Jianping Lu, Yixin Wang, Yubin Xia, Meng Hu, Haowen Liu, Peng Xu, Licong Xu, Fu Bian, Xiaolong Gu, Lisha Zhang, Weilei Wang, Changyang Tu
In recent years, large language models(LLMs) have attracted significant attention due to their exceptional performance across a multitude of natural language process tasks, and have been widely applied in various fields.
no code implementations • 18 Sep 2022 • Jie Fang, Xiongwei Wu, DianChao Lin, Mengyun Xu, Huahua Wu, Xuesong Wu, Ting Bi
In addition, there are a large amount of other data, e. g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths.
no code implementations • 14 Aug 2020 • Jie Fang, Jian-Wu Lin, Shu-Tao Xia, Yong Jiang, Zhikang Xia, Xiang Liu
This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures.
no code implementations • 16 Jun 2020 • Jie Fang, Jian-Wu Lin
In this paper, we propose to use neural networks to represent these indicators and train a large network constructed of smaller networks as feature layers to fine-tune the prior knowledge represented by the indicators.
no code implementations • 26 Dec 2019 • Jie Fang, Shu-Tao Xia, Jian-Wu Lin, Zhikang Xia, Xiang Liu, Yong Jiang
This paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network structure which can automatically construct diversified financial technical indicators based on prior knowledge.
no code implementations • 8 Dec 2019 • Jie Fang, Shu-Tao Xia, Jian-Wu Lin, Yong Jiang
According to neural network universal approximation theorem, pre-training can conduct a more effective and explainable evolution process.