PAL: A fast DNN optimization method based on curvature information. - Maximus Mutschler; Andreas Zell (CoRR, 2019)

Parabolic Approximation Line Search for DNNs. - Maximus Mutschler; Andreas Zell (NeurIPS, 2020)

A straightforward line search approach on the expected empirical loss for stochastic deep learning problems. - Maximus Mutschler; Andreas Zell (CoRR, 2020)

Empirically Explaining SGD from a Line Search Perspective. - Maximus Mutschler; Andreas Zell (ICANN (2), 2021)

Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training. - Maximus Mutschler; Andreas Zell (CoRR, 2021)

What to expect of hardware metric predictors in NAS. - Kevin Alexander Laube; Maximus Mutschler; Andreas Zell (AutoML, 2022)

Empirics-based Line Searches for Deep Learning. - Maximus Mutschler (2023)