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cs.LG2025
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…
cs.LG2025
Saving Foundation Flow-Matching Priors for Inverse Problems
Yuxiang Wan, Ryan Devera, Wenjie Zhang +1
Foundation flow-matching (FM) models promise universal priors for solving inverse problems (IPs); yet today, they trail behind domain-specific and even untrained priors. \emph{How…
eess.IV2025
FMPlug: Plug-In Foundation Flow-Matching Priors for Inverse Problems
Yuxiang Wan, Ryan Devera, Wenjie Zhang +1
We present FMPlug, a novel plug-in framework that enhances foundation flow-matching (FM) priors for solving ill-posed inverse problems. Unlike traditional approaches that rely on d…