no code implementations • • 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.
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.
Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications.
The past decade has witnessed significant advances in time series modeling with deep learning.
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world.
Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions.
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.
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes.
Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making.
Advances in representation learning have led to great success in understanding and generating data in various domains.
Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods.
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.
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.
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.
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.