no code implementations • 29 Dec 2023 • Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao, Shan Zhong, Meikang Qiu
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science.
no code implementations • 15 Sep 2023 • Jiacheng He, Shan Zhong, Bei Peng, Gang Wang, Qizhen Wang
In multi-target tracking (MTT), non-Gaussian measurement noise from sensors can diminish the performance of the Gaussian-assumed Gaussian mixture probability hypothesis density (GM-PHD) filter.
no code implementations • 6 Sep 2023 • Zuxuan Zhang, Gang Wang, Jiacheng He, Shan Zhong
The estimation of non-Gaussian measurement noise models is a significant challenge across various fields.
no code implementations • 3 Aug 2023 • Prateek Verma, Shan Zhong, Xiaoyu Liu, Adithya Rajan
Query autocomplete (QAC) also known as typeahead, suggests list of complete queries as user types prefix in the search box.
no code implementations • 14 Jan 2023 • Jiacheng He, Hongwei Wang, Gang Wang, Shan Zhong, Bei Peng
Outliers and impulsive disturbances often cause heavy-tailed distributions in practical applications, and these will degrade the performance of Gaussian approximation smoothing algorithms.
1 code implementation • 24 Aug 2021 • Shan Zhong, David B. Hitchcock
We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices.
9 code implementations • 19 Nov 2018 • Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang, Anwar Walid
We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.