118 citations · 153 across the 5 of their papers we have counts for
5 papers
Parallel Complex Diffusion for Scalable Time Series Generation
Rongyao Cai, Yuxi Wan, Kexin Zhang +4
Diffusion models learn data distributions indirectly through denoising, making the difficulty of generative modeling closely tied to the dependency structure of data. For time seri…
The Procrustean Bed of Time Series: The Optimization Bias in Point-wise Loss Functions
Rongyao Cai, Yuxi Wan, Kexin Zhang +6
Intuitively, a more deterministic time series should be easier to forecast. However, point-wise loss functions (e.g., MSE and MAE), serving as differentiable surrogates for the ide…
IG2: Integrated Gradient on Iterative Gradient Path for Feature Attribution
Yue Zhuo, Zhiqiang Ge
Feature attribution explains Artificial Intelligence (AI) at the instance level by providing importance scores of input features' contributions to model prediction. Integrated Grad…
Directed Acyclic Graphs With Tears
Zhichao Chen, Zhiqiang Ge
Bayesian network is a frequently-used method for fault detection and diagnosis in industrial processes. The basis of Bayesian network is structure learning which learns a directed…
Latent Variable Models in the Era of Industrial Big Data: Extension and Beyond
Xiangyin Kong, Xiaoyu Jiang, Bingxin Zhang +2
A rich supply of data and innovative algorithms have made data-driven modeling a popular technique in modern industry. Among various data-driven methods, latent variable models (LV…