3 papers
cs.LG2025
Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets
Muhao Guo, Haoran Li, Yang Weng
Neural ordinary differential equations (NODE) have garnered significant attention for their design of continuous-depth neural networks and the ability to learn data/feature dynamic…
cs.LG2025
Modeling Time Series Dynamics with Fourier Ordinary Differential Equations
Muhao Guo, Yang Weng
Neural ODEs (NODEs) have emerged as powerful tools for modeling time series data, offering the flexibility to adapt to varying input scales and capture complex dynamics. However, t…
cs.LG2025
From Imperfect Signals to Trustworthy Structure: Confidence-Aware Inference from Heterogeneous and Reliability-Varying Utility Data
Haoran Li, Lihao Mai, Muhao Guo +4
Accurate distribution grid topology is essential for reliable modern grid operations. However, real-world utility data originates from multiple sources with varying characteristics…