Machine Learning

Machine Learning

Development of solutions involving data and knowledge

Machine Learning

In the Machine Learning field of competence, we view ourselves as trailblazers, both in terms of research, as well as in the beneficial application of this technology in industrial environments. Machine learning utilizes predefined examples with the aim of solving issues. Although machines do not possess the same capacity for learning as humans, they can be designed such that can help us in complex environments, such as autonomous driving, medical diagnostics or predictive maintenance.

Our research centers around two areas in particular: reinforcement learning and representation learning. We also focus on the development of strategies that increase the extent to which data-driven approaches are able to adapt to changing conditions, thus making these approaches a more constant factor than changes. The fields of application include image and language processing, autonomous navigation and recommender systems. We have set out to acquire an in-depth understanding of machine learning, plus its concepts and application scenarios, which is a prerequisite for the efficient deployment of these technologies in real environments.

In order to simplify the transfer of technology, our field of competence operates the “One Stop Shop” Machine Learning Lab (One-ML), where we offer our partners and other interested organizations in industry, training and education and society, a range of services from a single source. These services include technical coaching in the area of machine learning, consulting on various topics - from theory and development to implementation - as well as public presentations outlining the opportunities and risks associated with machine learning.

Further information

Whitepaper Human-centric Machine Learning
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Dr. Hao Shen

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Dr. Hao Shen

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  • 2022 Laplace Approximation for Real-time Uncertainty Estimation in Object Detection Ming Gui, Tianming Qiu, Fridolin Bauer and Hao Shen 25th IEEE International Conference on Intelligent Transportation Systems (ITSC), ():, 2022. Details BIB
  • 2022 SViT: Hybrid Vision Transformer Models with Scattering Transform Tianming Qiu, Ming Gui, Cheng Yan, Ziqing Zhao and Hao Shen 32nd IEEE International Workshop on Machine Learning for Signal Processing (MLSP), ():, 2022. Details BIB
  • 2021 Low-Redundancy Realization of Safety-Critical Automotive Functions Simon Barner, Stefan Matthes, Holger Dormann, Angelika Schingale, Eberhard Kaulfersch, Michael Eichhorst, Lutz Scheiter, Holger Schmidt and Jürgen Gebert In MikroSystemTechnik (MST) Kongress, VDE Verlag GmbH Berlin Offenbach.. Details URL BIB
  • 2021 Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces Alexander Sagel, Julian Wörmann and Hao Shen In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3755–3759, Details DOI BIB
  • 2021 Neural Network and Correlation based Earth-Fault Localization utilizing a Digital Twin of a Medium-Voltage Grid Julian Wörmann, Melanie Urban, David Grubinger, Nuno Silva and Hans-Peter Schwefel In e-Energy '21: Proceedings of the Twelfth ACM International Conference on Future Energy Systems, pages 249–253, Details DOI BIB
  • 2020 Draw with Me: Human-in-the-Loop for Image Restoration Thomas Weber, Zhiwei Han, Stefan Matthes, Heinrich Hußmann and Yuanting Liu In the 43rd German Conference on AI, pages 245-248, Springer. Details URL BIB
  • 2018 Towards a Mathematical Understanding of the Difficulty in Learning with Feedforward Neural Networks Hao Shen Proceedings of the 31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ():811–820, 2018. Details BIB