no code implementations • NAACL 2022 • Jiangang Bai, Yujing Wang, Hong Sun, Ruonan Wu, Tianmeng Yang, Pengfei Tang, Defu Cao, Mingliang Zhang1, Yunhai Tong, Yaming Yang, Jing Bai, Ruofei Zhang, Hao Sun, Wei Shen
Large-scale pre-trained language models have attracted extensive attentions in the research community and shown promising results on various tasks of natural language processing.
no code implementations • 15 Apr 2024 • Xiongye Xiao, Gengshuo Liu, Gaurav Gupta, Defu Cao, Shixuan Li, Yaxing Li, Tianqing Fang, Mingxi Cheng, Paul Bogdan
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems.
1 code implementation • 14 Feb 2024 • Xiongye Xiao, Chenyu Zhou, Heng Ping, Defu Cao, Yaxing Li, Yizhuo Zhou, Shixuan Li, Paul Bogdan
Prior studies on the emergence in large models have primarily focused on how the functional capabilities of large language models (LLMs) scale with model size.
no code implementations • 8 Feb 2024 • Yizhou Zhang, Lun Du, Defu Cao, Qiang Fu, Yan Liu
Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications.
1 code implementation • 8 Oct 2023 • Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu
The past decade has witnessed significant advances in time series modeling with deep learning.
no code implementations • 27 Sep 2023 • Xiongye Xiao, Gengshuo Liu, Gaurav Gupta, Defu Cao, Shixuan Li, Yaxing Li, Tianqing Fang, Mingxi Cheng, Paul Bogdan
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world.
no code implementations • 15 Apr 2023 • Yizhou Zhang, Loc Trinh, Defu Cao, Zijun Cui, Yan Liu
Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions.
no code implementations • 4 Mar 2023 • Defu Cao, James Enouen, Yujing Wang, Xiangchen Song, Chuizheng Meng, Hao Niu, Yan Liu
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc.
1 code implementation • 4 Mar 2023 • Xiongye Xiao, Defu Cao, Ruochen Yang, Gaurav Gupta, Gengshuo Liu, Chenzhong Yin, Radu Balan, Paul Bogdan
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes.
no code implementations • 19 Feb 2023 • Defu Cao, James Enouen, Yan Liu
Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making.
no code implementations • CVPR 2023 • Defu Cao, Zhaowen Wang, Jose Echevarria, Yan Liu
Advances in representation learning have led to great success in understanding and generating data in various domains.
no code implementations • 17 Nov 2022 • Defu Cao, Yousef El-Laham, Loc Trinh, Svitlana Vyetrenko, Yan Liu
Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods.
no code implementations • 14 Oct 2022 • Yizhou Zhang, Defu Cao, Yan Liu
To address these issues, in this paper, we build up a causal framework that model the causal effect of misinformation from the perspective of temporal point process.
no code implementations • 31 Mar 2022 • Chuizheng Meng, Sungyong Seo, Defu Cao, Sam Griesemer, Yan Liu
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results.
BIG-bench Machine Learning Physics-informed machine learning
no code implementations • 5 Jun 2021 • Defu Cao, Jiachen Li, Hengbo Ma, Masayoshi Tomizuka
To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain.
2 code implementations • NeurIPS 2020 • Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
2 code implementations • 4 Sep 2020 • Hang Zhao, Yujing Wang, Juanyong Duan, Congrui Huang, Defu Cao, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.