1 citations · 1 across the 3 of their papers we have counts for
4 papers
CP Loss: Channel-wise Perceptual Loss for Time Series Forecasting
Yaohua Zha, Chunlin Fan, Peiyuan Liu +4
Multi-channel time-series data, prevalent across diverse applications, is characterized by significant heterogeneity in its different channels. However, existing forecasting models…
Efficient Differentiable Approximation of Generalized Low-rank Regularization
Naiqi Li, Yuqiu Xie, Peiyuan Liu +3
Low-rank regularization (LRR) has been widely applied in various machine learning tasks, but the associated optimization is challenging. Directly optimizing the rank function under…
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
Yifan Hu, Guibin Zhang, Peiyuan Liu +6
Time series forecasting methods generally fall into two main categories: Channel Independent (CI) and Channel Dependent (CD) strategies. While CI overlooks important covariate rela…
Diffusion Prior Interpolation for Flexibility Real-World Face Super-Resolution
Jiarui Yang, Tao Dai, Yufei Zhu +3
Diffusion models represent the state-of-the-art in generative modeling. Due to their high training costs, many works leverage pre-trained diffusion models' powerful representations…