Dr. Maximus Mutschler

Background

Assignments class: Introduction to neural networks (Summer Semester 2018)
Assignments class: Introduction to technical computer science (Winter Semester 2018)
Assignments class: Deep Neural Networks (Summer Semester 2019)
Assignments class: Deep Neural Networks (Winter Semester 2019)
Supervision of a Software Engineering Practical Course about Deep Style Transfer (Summer Semester 2020)
Seminar Optimization and Architecture Search for Deep Neural Networks (Winter Semester 2020)
Supervision of a Software Engineering Practical Course about building a web application for Deep Style Transfer (Summer Semester 2021)
Assignments class: Deep Learning (Winter Semester 2021)

Administrator of the Training Center for Machine Learning Cluster (TCML-Cluster) with 40 nodes and 160 GPUs

2019	Bachelor thesis	Analysis of the Generalization Capability of a New Deep Neural Network Optimizer
2020	Bachelor thesis	Sensitivity Analysis of DNN Optimizers on different weight initializations
2020	Bachelor thesis	Forcing Deep Neural Networks to explore the Loss Landscape
2020	Master thesis	Adaptive Approximation niedrig dimensionaler Mannigfaltigkeiten zwischen lokalen Minima in den Fehlerlandschaften von Neuronalen Netzen

Softwareplagiatserkennung auf Java-Bytecodebasis
Bachelor's thesis, University of Heidelberg and University of Heilbronn, June 2014

[1]	Maximus Mutschler and Andreas Zell. “Parabolic Approximation Line Search for DNNs” . Accepted at NeurIPS 2020.
[2]	Maximus Mutschler and Andreas Zell. “A straightforward line search approach on the expected empirical loss for stochastic deep learning problems” (2020). (discontinued work)
[3]	Maximus Mutschler and Andreas Zell. "Emperically explaining SGD from a line search perspective" (2021). Accepted at ICANN 2021.
[4]	Maximus Mutschler, Kevin Laube and Andreas Zell. "Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training" (2021). Accepted at NeurIPS 2021 Optimization Workshop.