no code implementations • 14 Feb 2022 • Shengyu Feng, Baoyu Jing, Yada Zhu, Hanghang Tong
Contrastive learning is an effective unsupervised method in graph representation learning.
no code implementations • 2 Dec 2021 • Zixuan Yuan, Yada Zhu, Wei zhang, Ziming Huang, Guangnan Ye, Hui Xiong
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.
no code implementations • ACL 2021 • Wei zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
no code implementations • 9 Jun 2021 • Wei zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang
In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.
no code implementations • 19 May 2021 • Lecheng Zheng, Yada Zhu, Jingrui He, JinJun Xiong
With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity.
1 code implementation • 15 Feb 2021 • Baoyu Jing, Hanghang Tong, Yada Zhu
We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN).
1 code implementation • 9 Feb 2020 • Yunan Ye, Hengzhi Pei, Boxin Wang, Pin-Yu Chen, Yada Zhu, Jun Xiao, Bo Li
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
no code implementations • 17 Oct 2019 • Di Chen, Yada Zhu, Xiaodong Cui, Carla P. Gomes
Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making.
no code implementations • 19 Sep 2019 • Giovanni Mariani, Yada Zhu, Jianbo Li, Florian Scheidegger, Roxana Istrate, Costas Bekas, A. Cristiano I. Malossi
Sound financial theories demonstrate that in an efficient marketplace all information available today, including expectations on future events, are represented in today prices whereas future price trend is driven by the uncertainty.
Computational Finance Statistical Finance