Search Results for author: Sebastian Schmitt

Found 22 papers, 4 papers with code

Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware

no code implementations28 Mar 2023 Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt

In contrast to other optimization methods such as genetic algorithms or stochastic searches, the SNPE algorithms belongs to the class of approximate Bayesian computing (ABC) methods and estimates the posterior distribution of the model parameters; access to the posterior allows classifying the confidence in parameter estimations and unveiling correlation between model parameters.

Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces

no code implementations13 Jan 2023 Felix Lanfermannn, Sebastian Schmitt, Patricia Wollstadt

To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.

Decision Making energy management

Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives

no code implementations25 Aug 2022 Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer

To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives.

Concept Identification for Complex Engineering Datasets

no code implementations9 Jun 2022 Felix Lanfermann, Sebastian Schmitt

In this work, an approach to define meaningful and consistent concepts in an existing engineering dataset is presented.

Decision Making

Recent Advances in Bayesian Optimization

no code implementations7 Jun 2022 Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer

Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency.


Stream-based Active Learning with Verification Latency in Non-stationary Environments

1 code implementation14 Apr 2022 Andrea Castellani, Sebastian Schmitt, Barbara Hammer

Furthermore, we propose a drift-dependent dynamic budget strategy, which uses a variable distribution of the labelling budget over time, after a detected drift.

Active Learning

Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory

no code implementations10 Dec 2021 Patricia Wollstadt, Sebastian Schmitt

We thus demonstrate the power of novel information-theoretic approaches in identifying relevant parameters in optimization runs and highlight how these methods avoid the selection of redundant parameters, while detecting interactions that result in synergistic contributions of multiple parameters.

Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times

no code implementations30 Aug 2021 Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer

Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time.

Transfer Learning

Task-Sensitive Concept Drift Detector with Constraint Embedding

1 code implementation16 Aug 2021 Andrea Castellani, Sebastian Schmitt, Barbara Hammer

In the proposed framework, the actual method to detect a change in the statistics of incoming data samples can be chosen freely.

Metric Learning

A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition

no code implementations10 May 2021 Patricia Wollstadt, Sebastian Schmitt, Michael Wibral

We argue that this lack is inherent to classical information theory which does not provide measures to decompose the information a set of variables provides about a target into unique, redundant, and synergistic contributions.

PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data

no code implementations11 Mar 2021 Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck, Nan Pu, Wei Chen, Michael Lew

The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated.

Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning

no code implementations12 Nov 2020 Andrea Castellani, Sebastian Schmitt, Stefano Squartini

The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery.

Anomaly Detection Weakly-supervised Learning

The Operating System of the Neuromorphic BrainScaleS-1 System

no code implementations30 Mar 2020 Eric Müller, Sebastian Schmitt, Christian Mauch, Sebastian Billaudelle, Andreas Grübl, Maurice Güttler, Dan Husmann, Joscha Ilmberger, Sebastian Jeltsch, Jakob Kaiser, Johann Klähn, Mitja Kleider, Christoph Koke, José Montes, Paul Müller, Johannes Partzsch, Felix Passenberg, Hartmut Schmidt, Bernhard Vogginger, Jonas Weidner, Christian Mayr, Johannes Schemmel

We present operation and development methodologies implemented for the BrainScaleS-1 neuromorphic architecture and walk through the individual components of BrainScaleS OS constituting the software stack for BrainScaleS-1 platform operation.

Exploring the fitness landscape of a realistic turbofan rotor blade optimization

no code implementations16 Oct 2019 Jakub Kmec, Sebastian Schmitt

The choices to be made in the setup of the optimization process strongly influence this mapping and thus are expected to have a profound influence on the achievable result.

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