Connectionists: [CFP] Special Session: Efficient and Resilient Machine Learning for Industrial Applications @ESANN2026

[Wissmann, Philipp]

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CALL FOR PAPERS (ESANN 2026)
European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning: https://www.esann.org/
More details on the Special Session “Efficient and Resilient Machine Learning for Industrial Applications": https://www.esann.org/special-sessions#session6
Bruges, Belgium, 22-24 April 2026
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****** Call For Papers ******

The integration of AI models into industrial applications holds significant opportunities for process optimization, automation and quality control, all contributing to increased efficiency, reduced costs, and improved product reliability. However, realizing these benefits involves overcoming challenges related to computational efficiency, system transparency, and reliability, which can hinder successful deployment and adoption. This special session delves into the complexities faced by real-world industrial applications utilizing intelligent technologies, emphasizing the need for efficient and resilient methods amid the rapid growth of these systems.

As an example, reinforcement learning offers the potential to enhance control and decision-making but requires overcoming obstacles such as learning from offline data and limited exploration capabilities. At the same time, recent advances in generative AI and the resulting foundation models offer promising avenues for promoting scalability and broad application across diverse industrial contexts. In this context insightful and fair benchmarking against conventional methods remains a key challenge to effectively guide stakeholder decisions. Moreover, building trust and clarity in AI systems is crucial for successful adoption. This calls for explainable, interpretable, and trustworthy AI techniques to align operations with human expectations and regulatory requirements. Furthermore, the session will explore strategies to develop AI systems that are data-efficient and flexible, minimizing data requirements while maintaining adaptability to changing environments. Together, these aspects aim to balance innovation with practical application in order to maximize the effectiveness of AI in industrial environments.

Topics of interest include, but are not limited to:

  *   Industrial challenges for AI
  *   ML applications in industrial manufacturing
  *   Offline reinforcement learning
  *   Safe reinforcement learning
  *   Foundation models/GenAI for industry applications
  *   Benchmarking conventional methods and foundation models
  *   Explainable and trustworthy AI
  *   Data efficient simulation and forecasting
  *   ML-assisted process modeling, simulation and monitoring
  *   Multimodal modeling and anomaly detection
  *   Modeling uncertainty in industrial settings
  *   Deep-learning-based time-series and signal processing


****** Important Dates ******
Paper Submissions: 19 November 2025
Paper Acceptance Notifications: 23 January 2026
Conference: 22-24 April 2026

****** Session Organisers ******

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Philipp Wissmann (Siemens AG, Germany)
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Marc Weber (Siemens AG, Germany)
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Simon Leszek (TU Berlin, Germany)
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Philip Naumann (TU Berlin, Germany)
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Daniel Hein (Siemens AG, Germany)
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Steffen Udluft (Siemens AG, Germany)
  *
Thomas Runkler (TU Munich, Germany)




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