2 citations · 5 across the 20 of their papers we have counts for
23 papers
FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning
Tristan Gottwald, Maximilian Schier, Melanie Schaller +1
Event cameras generate asynchronous, high-frequency data streams offering spatially sparse information at lower latency than traditional cameras. In principle, these properties sho…
Reinforcement learning for ion shuttling on trapped-ion quantum computers
Maximilian Schier, Lea Richtmann, Christian Staufenbiel +4
Scalable trapped-ion quantum computing is commonly realized with modular chips that feature distinct zones with specific functionalities, such as storage, state preparation, and ga…
Numerical field optimization for enhanced efficiency in time-reversible gradient computation of open-source GPU-accelerated FDTD simulations
Yannik Mahlau, Lukas Berg, Bodo Rosenhahn
Finite-difference time-domain (FDTD) simulations often involve physical quantities spanning multiple orders of magnitude, such as the speed of light or electromagnetic field amplit…
Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics
Yannik Mahlau, Yannick Augenstein, Tyler W. Hughes +2
Inverse design, particularly geometric shape optimization, provides a systematic approach for developing high-performance nanophotonic devices. While numerous optimization algorith…
Stochastic Neural Networks for Quantum Devices
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stocha…
Naga: Vedic Encoding for Deep State Space Models
Melanie Schaller, Nick Janssen, Bodo Rosenhahn
This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed method introduces a bidirectional re…
A Rotation-Invariant Embedded Platform for (Neural) Cellular Automata
Dominik Woiwode, Jakob Marten, Bodo Rosenhahn
This paper presents a rotation-invariant embedded platform for simulating (neural) cellular automata (NCA) in modular robotic systems. Inspired by previous work on physical NCA, we…
Neural Guided Sampling for Quantum Circuit Optimization
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the l…
Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models
Nick Janssen, Melanie Schaller, Bodo Rosenhahn
Understanding the robustness of deep learning models for multivariate long-term time series forecasting (M-LTSF) remains challenging, as evaluations typically rely on real-world da…
Pruning by Block Benefit: Exploring the Properties of Vision Transformer Blocks during Domain Adaptation
Patrick Glandorf, Bodo Rosenhahn
Vision Transformer have set new benchmarks in several tasks, but these models come with the lack of high computational costs which makes them impractical for resource limited hardw…
Multi-Agent Reinforcement Learning for Inverse Design in Photonic Integrated Circuits
Yannik Mahlau, Maximilian Schier, Christoph Reinders +3
Inverse design of photonic integrated circuits (PICs) has traditionally relied on gradientbased optimization. However, this approach is prone to end up in local minima, which resul…
Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection
Mathis Kruse, Bodo Rosenhahn
With more well-performing anomaly detection methods proposed, many of the single-view tasks have been solved to a relatively good degree. However, real-world production scenarios o…
S4D-Bio Audio Monitoring of Bone Cement Disintegration in Pulsating Fluid Jet Surgery under Laboratory Conditions
Melanie Schaller, Sergej Hloch, Akash Nag +5
This study investigates a pulsating fluid jet as a novel precise, minimally invasive and cold technique for bone cement removal. We utilize the pulsating fluid jet device to remove…
AutoML for Multi-Class Anomaly Compensation of Sensor Drift
Melanie Schaller, Mathis Kruse, Antonio Ortega +2
Addressing sensor drift is essential in industrial measurement systems, where precise data output is necessary for maintaining accuracy and reliability in monitoring processes, as…
Optimization Driven Quantum Circuit Reduction
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
Implementing a quantum circuit on specific hardware with a reduced available gate set is often associated with a substantial increase in the length of the equivalent circuit. This…
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification
Christoph Reinders, Frederik Schubert, Bodo Rosenhahn
Training deep neural networks requires datasets with a large number of annotated examples. The collection and annotation of these datasets is not only extremely expensive but also…
A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures
Yannik Mahlau, Frederik Schubert, Konrad Bethmann +5
We introduce an efficient open-source python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Leve…
Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery
Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn +1
Monocular 3D human pose and shape estimation is an inherently ill-posed problem due to depth ambiguities, occlusions, and truncations. Recent probabilistic approaches learn a distr…
Quantized Inverse Design for Photonic Integrated Circuits
Frederik Schubert, Yannik Mahlau, Konrad Bethmann +5
The inverse design of photonic integrated circuits (PICs) presents distinctive computational challenges, including their large memory requirements. Advancements in the two-photon p…
SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection
Mathis Kruse, Marco Rudolph, Dominik Woiwode +1
Detecting anomalies in images has become a well-explored problem in both academia and industry. State-of-the-art algorithms are able to detect defects in increasingly difficult set…
Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games
Yannik Mahlau, Frederik Schubert, Bodo Rosenhahn
The combination of self-play and planning has achieved great successes in sequential games, for instance in Chess and Go. However, adapting algorithms such as AlphaZero to simultan…
Quantum Normalizing Flows for Anomaly Detection
Bodo Rosenhahn, Christoph Hirche
A Normalizing Flow computes a bijective mapping from an arbitrary distribution to a predefined (e.g. normal) distribution. Such a flow can be used to address different tasks, e.g.…
Q-SENN: Quantized Self-Explaining Neural Networks
Thomas Norrenbrock, Marco Rudolph, Bodo Rosenhahn
Explanations in Computer Vision are often desired, but most Deep Neural Networks can only provide saliency maps with questionable faithfulness. Self-Explaining Neural Networks (SEN…