Johann Schmidt

Otto-von-Guericke University Magdeburg | OvGU

Traffic signal control plays a crucial role in urban mobility. However, existing methods often struggle to generalize beyond their training environments to unseen scenarios with varying traffic dynamics. We present TransferLight, a novel framework designed for robust generalization across road-networks, diverse traffic conditions and intersection g...

Deep neural networks are applied in more and more areas of everyday life. However, they still lack essential abilities, such as robustly dealing with spatially transformed input signals. Approaches to mitigate this severe robustness issue are limited to two pathways: Either models are implicitly regularised by increased sample variability (data aug...

Inspired by the success of Simulated Annealing in physics, we transfer insights and adaptations to the scheduling domain, specifically addressing the one-stage job scheduling problem with an arbitrary number of parallel machines. In optimization, challenges arise from local optima, plateaus in the loss surface, and computationally complex Hamiltoni...

Nearly all state of the art vision models are sensitive to image rotations. Existing methods often compensate for missing inductive biases by using augmented training data to learn pseudo-invariances. Alongside the resource demanding data inflation process, predictions often poorly generalize. The inductive biases inherent to convolutional neural n...

The article investigates the application of NeuroEvolution of Augmenting Topologies (NEAT) to generate and parameterize artificial neural networks (ANN) on determining allocation and sequencing decisions in a two-stage hybrid flow shop scheduling environment with family setup times. NEAT is a machine-learning and neural architecture search algorith...

Scheduling still constitutes a challenging problem, especially for complex problem settings involving due dates and sequence-dependent setups. The majority of existing approaches use heuristics or meta-heuristics, like Genetic Algorithms or Reinforcement Learning. We show that a supervised learning framework can learn and generalize from generated...