AI research
● for the solutions of the future
We apply transformative AI research to real-world challenges, collaborating closely with partners from academia, business, and industry.
Our research partners:
Our mission
AI research for the challenges of today and tomorrow
For us, applied research and technological excellence means that our partners and customers receive solutions to their problems that are both innovative and practicable. In our research projects, we deal with the latest methods of machine learning. We strive to develop general solutions that can be used in a wide range of application areas.
Our main focus is on core German industries, such as manufacturing technologies and mechanical engineering, whose potential for the use of AI is far from exhausted. We are researching how knowledge and data in these areas can soon be used in the same intuitive way as text and image data can be used today.
Our main focus is on core German industries, such as manufacturing technologies and mechanical engineering, whose potential for the use of AI is far from exhausted. We are researching how knowledge and data in these areas can soon be used in the same intuitive way as text and image data can be used today.
In contrast to purely academic institutions and industrial R&D, we strive for a balanced connection between these two worlds by taking the freedom to dive deep into relevant research problems while being part of a highly professional and collaborative technical environment.
To add value to the scientific community and advance AI research as a whole, we not only use our results to deliver excellent solutions in the client business, but also publish and present them in leading journals and conferences.
To add value to the scientific community and advance AI research as a whole, we not only use our results to deliver excellent solutions in the client business, but also publish and present them in leading journals and conferences.
At Merantix Momentum, we initiate and participate in a variety of joint partnerships and scientific collaborations across different sectors and funding programs.
We have extensive experience in the design and management of research projects with a particular focus on collaborative research projects. If you are interested in working with us, please get in touch using our contact form!
We have extensive experience in the design and management of research projects with a particular focus on collaborative research projects. If you are interested in working with us, please get in touch using our contact form!
Due to the diversity of our customer projects, we try to cover a broad spectrum of machine learning research and always remain at the cutting edge of technology. At the same time, there are some areas on which our research is particularly focused:
- Learning with tabular and time series data, especially load and demand forecasts
- Interpretability, explainability, root cause analysis
- Embedding prior knowledge and domain expertise in learning processes
- Process optimization and simulation-based inference
- Learning with tabular and time series data, especially load and demand forecasts
- Interpretability, explainability, root cause analysis
- Embedding prior knowledge and domain expertise in learning processes
- Process optimization and simulation-based inference
Publications
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Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm (2024)
Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
Journal of Machine Learning Research
DeepRod: A human-in-the-loop system for automatic rodent behavior analysis (2024)
Adrian Loy, Miha Garafolj, Heike Schauerte, Hanna Behnke, Cyrille Charnier, Philipp Schwarz, Georg Rast, Thomas Wollmann
ICML 2024
Progressive Updates of Convolutional Neural Networks for Enhanced Reliability in Small Satellite Applications (2024)
Olga Kondrateva, Stefan Dietzel, Maximilian Schambach, Johannes Otterbach, Björn Scheuermann
Elsevier COMCOM Journal
Explainability and Interpretability in Electric Load Forecasting Using Machine Learning Techniques (2024)
Lukas Baur, Konstantin Ditschuneit, Maximilian Schambach, Can Kaymakci, Thomas Wollmann, Alexander Sauer
Energy and AI
Multiscale Neural Operators for Solving Time-Independent PDEs (2023)
Winfried Ripken, Lisa Coiffard, Felix Pieper, Sebastian Dziadzio
NeurIPS 2023
Towards Tabular Foundation Models - Status Quo, Challenges, and Opportunities (2023)
Maximilian Schambach
HAL preprint & SAP-internal publication
Self-distilled Representation Learning for Time Series (2023)
Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes Otterbach
NeurIPS 2023
Curve your Enthusiasm: Concurvity Regularization in Differentiable GAMs (2023)
Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes Otterbach, Martin Genzel
NeurIPS 2023
SECREDAS: Safe and (Cyber-)Secure Cooperative and Automated Mobility (2023)
Sebastian Gerres
IFAC 2023 - Full PDF at arxiv.org
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation (2023).
Alexander Koenig, Maximilian Schambach, Johannes S. Otterbach
CVPR 2023 - Full PDF at arxiv.org
Research projects
Cutting-Edge Results: Successful partnerships in the field of machine learning.
Our research pushes the boundaries. We bring our excellence in machine learning to publicly funded projects covering all industrial sectors. In partnerships with various stakeholders, we explore innovative solutions and discover new approaches to tackle real-world challenges.
Find out what we're working on.
Find out what we're working on.
Contact us
- Focus on research with tabular and time series data
- Solutions for the industrial challenges of today and tomorrow
- Integration of machine learning into established and new systems
- Long-standing, reliable partner in public and private consortium research
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