347 citations · 809 across the 25 of their papers we have counts for
25 papers
A supervised hybrid quantum machine learning solution to the emergency escape routing problem
Nathan Haboury, Mo Kordzanganeh, Sebastian Schmitt +6
Managing the response to natural disasters effectively can considerably mitigate their devastating impact. This work explores the potential of using supervised hybrid quantum machi…
From Clean Room to Machine Room: Commissioning of the First-Generation BrainScaleS Wafer-Scale Neuromorphic System
Hartmut Schmidt, José Montes, Andreas Grübl +9
The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic system for emulating large-scale networks of spiking neurons. Following a "physical modeli…
Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware
Jakob Kaiser, Raphael Stock, Eric Müller +2
The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons. When replicati…
Data re-uploading with a single qudit
Noah L. Wach, Manuel S. Rudolph, Fred Jendrzejewski +1
Quantum two-level systems, i.e. qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, higher dimensional quantu…
Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces
Felix Lanfermann, Sebastian Schmitt, Patricia Wollstadt
Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups…
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Multi-objective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multi-objective optimizat…
Recent Advances in Bayesian Optimization
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the dev…
Stream-based Active Learning with Verification Latency in Non-stationary Environments
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
Data stream classification is an important problem in the field of machine learning. Due to the non-stationary nature of the data where the underlying distribution changes over tim…
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Eric Müller, Elias Arnold, Oliver Breitwieser +20
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This wor…
Quantum Annealing for Industry Applications: Introduction and Review
Sheir Yarkoni, Elena Raponi, Thomas Bäck +1
Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies hav…
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is unten…
Task-Sensitive Concept Drift Detector with Constraint Embedding
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
Detecting drifts in data is essential for machine learning applications, as changes in the statistics of processed data typically has a profound influence on the performance of tra…
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
In complex industrial settings, it is common practice to monitor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system perfor…
A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition
Patricia Wollstadt, Sebastian Schmitt, Michael Wibral
Selecting a minimal feature set that is maximally informative about a target variable is a central task in machine learning and statistics. Information theory provides a powerful f…
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +2
Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to add…
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +3
Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes,…
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling
Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen +1
In the context of optimization approaches to engineering applications, time-consuming simulations are often utilized which can be configured to deliver solutions for various levels…
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning
Andrea Castellani, Sebastian Schmitt, Stefano Squartini
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologie…
hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
Philipp Spilger, Eric Müller, Arne Emmel +10
We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hard…
The Operating System of the Neuromorphic BrainScaleS-1 System
Eric Müller, Sebastian Schmitt, Christian Mauch +19
BrainScaleS-1 is a wafer-scale mixed-signal accelerated neuromorphic system targeted for research in the fields of computational neuroscience and beyond-von-Neumann computing. The…
Exploring the fitness landscape of a realistic turbofan rotor blade optimization
Jakub Kmec, Sebastian Schmitt
Aerodynamic shape optimization has established itself as a valuable tool in the engineering design process to achieve highly efficient results. A central aspect for such approaches…
Limits and trade-offs of topological network robustness
Christopher Priester, Sebastian Schmitt, Tiago P. Peixoto
We investigate the trade-off between the robustness against random and targeted removal of nodes from a network. To this end we utilize the stochastic block model to study ensemble…
Accelerated physical emulation of Bayesian inference in spiking neural networks
Akos F. Kungl, Sebastian Schmitt, Johann Klähn +21
The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the…
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
Sebastian Schmitt, Johann Klaehn, Guillaume Bellec +22
Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energ…