14 citations · 20 across the 5 of their papers we have counts for
5 papers
Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration
Moussa Kassem Sbeyti, Michelle Karg, Christian Wirth +2
Robustly and accurately localizing objects in real-world environments can be challenging due to noisy data, hardware limitations, and the inherent randomness of physical systems. T…
Efficient Contextual Preferential Bayesian Optimization with Historical Examples
Farha A. Khan, Tanmay Chakraborty, Jörg P. Dietrich +1
State-of-the-art multi-objective optimization often assumes a known utility function, learns it interactively, or computes the full Pareto front-each requiring costly expert input.…
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Julian Wörmann, Daniel Bogdoll, Christian Brunner +49
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. However, in real life applications…
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings
Gesina Schwalbe, Christian Wirth, Ute Schmid
One major drawback of deep convolutional neural networks (CNNs) for use in safety critical applications is their black-box nature. This makes it hard to verify or monitor complex,…
Preference-Based Monte Carlo Tree Search
Tobias Joppen, Christian Wirth, Johannes Fürnkranz
Monte Carlo tree search (MCTS) is a popular choice for solving sequential anytime problems. However, it depends on a numeric feedback signal, which can be difficult to define. Real…