no code implementations • 24 Feb 2024 • Haotian Xia, Zhengbang Yang, Yuqing Wang, Rhys Tracy, Yun Zhao, Dongdong Huang, Zezhi Chen, Yan Zhu, Yuan-Fang Wang, Weining Shen
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP).
1 code implementation • 26 Sep 2023 • Haotian Xia, Rhys Tracy, Yun Zhao, Yuqing Wang, Yuan-Fang Wang, Weining Shen
Our frameworks combine setting ball trajectory recognition with a novel set trajectory classifier to generate comprehensive and advanced statistical data.
no code implementations • 28 Jun 2023 • Shiwei Lan, Mirjeta Pasha, Shuyi Li, Weining Shen
Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast.
no code implementations • 1 Jan 2021 • Gege Zhang, Gangwei Li, Weining Shen, Huixin Zhang, Weidong Zhang
Expressivity plays a fundamental role in evaluating deep neural networks, and it is closely related to understanding the limit of performance improvement.
no code implementations • 11 Apr 2020 • Yichi Zhang, Weining Shen, Dehan Kong
Covariance estimation for matrix-valued data has received an increasing interest in applications.
no code implementations • 24 Sep 2018 • Wei Hu, Weining Shen, Hua Zhou, Dehan Kong
We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies.
no code implementations • 22 Oct 2017 • Shan Suthaharan, Weining Shen
In this paper, we proposed a nonlinear parametric perturbation model that transforms the input feature patterns to a set of elliptical patterns, and studied the performance degradation issues associated with random forest classification technique using both the input and transform domain features.