Dr.-Ing. Richard Meyes, M.Sc.
Scientific Researcher
Head of the Research Field "Interpretable Learning Models"
Area of Research:
- Artificial Intelligence and Machine Learning for Industrial Appliations
- Predictive Analysis of Time Series Data in Industrial Sensor Systems
- Structured Representations in Artificial Neural Networks
Biography
Dr.-Ing. Richard Meyes has been a research associate at the Institute for Technologies and Management of Digital Transformation at the University of Wuppertal since December 2018. His research focuses on the development and investigation of artificial intelligence methods, with a focus on artificial neural networks, in various application fields, including automotive and manufacturing.
Publications
- 2021
- Meyes, R., Hütten, N., & Meisen, T. (2021). "Transparent and Interpretable Failure Prediction of Sensor Time Series Data with Convolutional Neural Networks" , Procedia CIRP , 104 , 1446—1451.
- 2020
- Meyes, R., Schneider, M., & Meisen, T. (2020). "How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents" .
- Meyes, R., Waubert-de-Puiseau, C., Posada-Moreno, A., & Meisen, T. (2020). "Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations" .
- 2019
- Meyes, R., Donauer, J., Schmeing, A., & Meisen, T. (2019). "A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data" , Procedia Manufacturing , 34 , 789—797.
- Meyes, R., Lu, M., Waubert-de-Puiseau, C., & Meisen, T. (2019). "Ablation Studies in Artificial Neural Networks" , arXiv arXiv:1901.08644 .
