Dr.-Ing. Richard Meyes, M.Sc.
Wissenschaftlicher Mitarbeiter
Leiter des Forschungsbereichs "Interpretable Learning Models"
Forschungsinteressen:
- Artificial Intelligence and Machine Learning for Industrial Appliations
- Predictive Analysis of Time Series Data in Industrial Sensor Systems
- Structured Representations in Artificial Neural Networks
Biographie
Dr.-Ing. Richard Meyes ist seit Dezember 2018 wissenschaftlicher Mitarbeiter am Institute for Technologies and Management of Digital Transformation an der Bergischen Universität Wuppertal. Seine Forschungsschwerpunkte liegen in der Entwicklung und Untersuchung von Methoden der künstlichen Intelligenz, mit Fokus auf künstliche neuronale Netze, in verschiedenen Anwendungsfeldern, darunter Automotive und Produktion.
Publikationen
- 2022
- Alves-Gomes, M., Meyes, R., Meisen, P., & Meisen, T. (2022). "Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using Embeddings" in Proceedings of the 31st ACM International Conference on Information & Knowledge Management , New York, NY, USA : {Association for Computing Machinery} 2873--2882.
ISBN: 9781450392365
- Alves-Gomes, M., Meyes, R., Meisen, P., & Meisen, T. (2022). "Will This Online Shopping Session Succeed? Predicting Customer’s Purchase Intention Using Embeddings" in Proceedings of the 31st ACM International Conference on Information & Knowledge Management , New York, NY, USA : Association for Computing Machinery 2873—2882.
ISBN: 9781450392365
- 2021
- Ekeris, T., Meyes, R., & Meisen, T. (2021). "Discovering Heuristics And Metaheuristics For Job Shop Scheduling From Scratch Via Deep Reinforcement Learning" in Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL~2021) .
- 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" .
