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cs.LG2026
Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions
Ha Dang, Sebastian Schmidt, Juergen Hesser
Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently continuous representations struggle…
cs.LG2026
Amplified Patch-Level Differential Privacy for Free via Random Cropping
Kaan Durmaz, Jan Schuchardt, Sebastian Schmidt +1
Random cropping is one of the most common data augmentation techniques in computer vision, yet the role of its inherent randomness in training differentially private machine learni…
cs.LG2025
Effective Data Pruning through Score Extrapolation
Sebastian Schmidt, Prasanga Dhungel, Christoffer Löffler +3
Training advanced machine learning models demands massive datasets, resulting in prohibitive computational costs. To address this challenge, data pruning techniques identify and re…