Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Ji, Taoran"'
Autor:
Ji, Taoran
Information mining and knowledge learning from sequential data is a field of growing importance in both industrial and academic fields. Sequential data, which is the natural representation format of the information flow in many applications, usually
Externí odkaz:
http://hdl.handle.net/10919/108141
Autor:
Wang, Shengkun, Ji, Taoran, Wang, Linhan, Sun, Yanshen, Liu, Shang-Ching, Kumar, Amit, Lu, Chang-Tien
The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language mo
Externí odkaz:
http://arxiv.org/abs/2409.08281
Autor:
Wang, Shengkun, Ji, Taoran, He, Jianfeng, Almutairi, Mariam, Wang, Dan, Wang, Linhan, Zhang, Min, Lu, Chang-Tien
Stock volatility prediction is an important task in the financial industry. Recent advancements in multimodal methodologies, which integrate both textual and auditory data, have demonstrated significant improvements in this domain, such as earnings c
Externí odkaz:
http://arxiv.org/abs/2407.18324
The fairness and trustworthiness of Large Language Models (LLMs) are receiving increasing attention. Implicit hate speech, which employs indirect language to convey hateful intentions, occupies a significant portion of practice. However, the extent t
Externí odkaz:
http://arxiv.org/abs/2402.11406
Predicting stock market is vital for investors and policymakers, acting as a barometer of the economic health. We leverage social media data, a potent source of public sentiment, in tandem with macroeconomic indicators as government-compiled statisti
Externí odkaz:
http://arxiv.org/abs/2312.03758
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of
Externí odkaz:
http://arxiv.org/abs/2310.18706
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Autor:
Chen, Zhiqian, Chen, Fanglan, Zhang, Lei, Ji, Taoran, Fu, Kaiqun, Zhao, Liang, Chen, Feng, Wu, Lingfei, Aggarwal, Charu, Lu, Chang-Tien
Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theori
Externí odkaz:
http://arxiv.org/abs/2107.10234
Background: This study aims to evaluate the Chicago Teen Pregnancy Prevention Initiative delivery optimization outcomes given policy-neutral and policy-focused approaches to deliver this program to at-risk teens across the City of Chicago. Methods: W
Externí odkaz:
http://arxiv.org/abs/2006.04029
Autor:
Chen, Zhiqian, Chen, Fanglan, Zhang, Lei, Ji, Taoran, Fu, Kaiqun, Zhao, Liang, Chen, Feng, Wu, Lingfei, Aggarwal, Charu, Lu, Chang-Tien
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an extension of deep learning beyond these domains, graph neural network
Externí odkaz:
http://arxiv.org/abs/2002.11867
Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impac
Externí odkaz:
http://arxiv.org/abs/1911.08684