2 papers
math.OC2026
Robust and Fast Training via Per-Sample Clipping
Davide Nobile, Philipp Grohs
We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show that the resulting method, per-s…
stat.ML2024
The sampling complexity of learning invertible residual neural networks
Yuanyuan Li, Philipp Grohs, Philipp Petersen
In recent work it has been shown that determining a feedforward ReLU neural network to within high uniform accuracy from point samples suffers from the curse of dimensionality in t…