no code implementations • 18 Feb 2024 • Min Zhang, Jianfeng He, Taoran Ji, Chang-Tien Lu
This serves as a reminder to carefully consider sensitivity and confidence in the pursuit of model fairness.
1 code implementation • 4 Dec 2023 • Shengkun Wang, Yangxiao Bai, Taoran Ji, Kaiqun Fu, Linhan Wang, Chang-Tien Lu
We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
1 code implementation • 28 Oct 2023 • Shengkun Wang, Yangxiao Bai, Kaiqun Fu, Linhan Wang, Chang-Tien Lu, Taoran Ji
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being.
1 code implementation • 21 Jul 2021 • Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu
Deep learning's performance has been extensively recognized recently.
no code implementations • 27 Feb 2020 • Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.
no code implementations • 20 Nov 2019 • Kaiqun Fu, Taoran Ji, Liang Zhao, Chang-Tien Lu
In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework.
1 code implementation • 22 May 2019 • Taoran Ji, Zhiqian Chen, Nathan Self, Kaiqun Fu, Chang-Tien Lu, Naren Ramakrishnan
For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent.