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20212026
most citedThe Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo

119 citations · 152 across the 29 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study

Gerhard Stenzel, Isabella Debelic, Michael Kölle +4

We extend directed quantum circuit synthesis (DQCS) with reinforcement learning from purely discrete gate selection to parameterized quantum state preparation with continuous singl…

cs.LG2026

Quantum King-Ring Domination in Chess: A QAOA Approach

Gerhard Stenzel, Michael Kölle, Tobias Rohe +4

The Quantum Approximate Optimization Algorithm (QAOA) is extensively benchmarked on synthetic random instances such as MaxCut, TSP, and SAT problems, but these lack semantic struct…

cs.LG2025

Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs

Tobias Rohe, Florian Burger, Michael Kölle +3

The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent…

cs.LG2024★ 3 cited

Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling

Michael Kölle, Afrae Ahouzi, Pascal Debus +4

Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with resp…

cs.LG2023

Compression of GPS Trajectories using Autoencoders

Michael Kölle, Steffen Illium, Carsten Hahn +3

The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data. Several compression methods have been developed…

cs.LG2021

Quantifying Multimodality in World Models

Andreas Sedlmeier, Michael Kölle, Robert Müller +2

Model-based Deep Reinforcement Learning (RL) assumes the availability of a model of an environment's underlying transition dynamics. This model can be used to predict future effect…