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Chair for Technologies and Management of Digital Transformation


Univ. Prof. Dr. Ing. Tobias Meisen

Interpretable Learning Models

Our research group Interpretable Learning Models (ILM) focusses on data driven approaches and corresponding analysis methods for time series and image data in production environments. Our goal is to facilitate the application of state-of-the-art learning models in complex and challenging scenarios such as condition monitoring, predictive quality, or predictive maintenance. Our main research topics cover the facilitation of transparency and interpretability of trained learning models, with a strong focus on artificial neural networks and deep learning models, to soften their inherent black-box character enabling their robust and reliable application in these environments. To this end, we rely on a broad variety of classical analytical methods from the field of signal processing, and modern learning mechanisms from the current state of the art in deep learning research. Additionally, we take inspiration from the research field of neuroscience aiming to promote a new perspective on artificial learning models as objects of interest in large scale empirical studies. We strongly believe that interactive and visual exploration of learning models and their corresponding data is the key for better transparency and interpretability. We envision the future of artificial learning models to be just as tangible, accessible, and easy to investigate as common everyday objects in the palm of our hands.

Main Topics

  • Sensor time series data analysis
  • Sensor signal labeling
  • Sensor signal classification
  • Sensor signal anomaly detection
  • Sensor signal forecasting
  • Sensor signal recostruction
  • Sensor signal similarity estimation
  • Sensor signal importance estimation
  • Sensor signal segmentation
  • Sensor signal motif extraction

Application Areas

  • Manufacturing and production scenarios
  • Condition monitoring
  • Predictive quality
  • Predictive maintenance
  • Soft sensors

Contact

Richard Meyes, M.Sc.

Selected relevant publications

References
Tristan Langer; Tobias Meisen
System Design to Utilize Domain Expertise for Visual Exploratory Data Analysis
Information, 12(4):140
2021

Keywords: research-interpretable-learning

Tristan Langer; Tobias Meisen
Visual Analytics for Industrial Sensor Data Analysis
Proceedings of the 23rd International Conference on Enterprise Information Systems , page 584--593.
Publisher: SCITEPRESS - Science and Technology Publications,
2021
ISBN: 978-989-758-509-8

Keywords: research-interpretable-learning

Richard Meyes; Constantin Waubert de Puiseau; Andres Posada-Moreno; Tobias Meisen
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
2020

Keywords: research-interpretable-learning

Richard Meyes; Moritz Schneider; Tobias Meisen
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents
2020

Keywords: research-interpretable-learning

Richard Meyes; Johanna Donauer; Andre Schmeing; Tobias Meisen
A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data
Procedia Manufacturing, 34:789--797
2019
ISSN: 2351-9789

Keywords: research-interpretable-learning

Richard Meyes; Melanie Lu; Constantin Waubert de Puiseau; Tobias Meisen
Ablation Studies in Artificial Neural Networks
arXiv arXiv:1901.08644,
2019

Keywords: research-interpretable-learning

Richard Meyes; Melanie Lu; Constantin Waubert de Puiseau; Tobias Meisen
Ablation Studies to Uncover Structure of Learned Representations in Artificial Neural Networks
Proceedings of the 2019 International Conference on Artificial Intelligence (ICAI),
2019

Keywords: research-interpretable-learning

Richard Meyes; Hasan Tercan; Tobias Meisen
Artificial Intelligence in Automotive Production
Mobility in a Globalised World 2018, 22:308--324
2019

Keywords: research-industrial-deep-learning;research-interpretable-learning

Tristan Langer; Tobias Meisen
Towards Utilizing Domain Expertise for Exploratory Data Analysis
Proceedings of the 12th International Symposium on Visual Information Communication and Interaction of VINCI'2019
Publisher: Association for Computing Machinery, New York, NY, USA
2019
ISBN: 9781450376266

Keywords: research-interpretable-learning

Peter Lillian; Richard Meyes; Tobias Meisen
Ablation of a Robot's Brain: Neural Networks Under a Knife
arXiv arXiv:1812.05687,
2018

Keywords: research-interpretable-learning

Marc Haßler; André Pomp; Christian Kohlschein; Tobias Meisen
STIDes Revisited-Tackling Global Time Shifts and Scaling
2018 International Conference on Innovations in Information Technology (IIT),
2018

Keywords: research-interpretable-learning

Marc Haßler; Sabina Jeschke; Tobias Meisen
Similarity Analysis of Time Interval Data Sets Regarding Time Shifts and Rescaling
Proceedings International work-conference on Time Series, (2):995--1006
2017

Keywords: research-interpretable-learning

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