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CALL FOR PAPERS (ESANN 2026)</div>
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European Symposium on Artificial Neural Networks, Computational</div>
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<span style="color: rgb(33, 33, 33);">Intelligence and Machine Learning: </span><span style="color: rgb(0, 120, 212);"><a href="https://www.esann.org/" class="ms-outlook-linkify" data-outlook-id="2b12b808-d100-4ebe-bb3a-9ebbb1e07e33" style="color: rgb(0, 120, 212);">https://www.esann.org/</a></span></div>
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<span style="color: rgb(33, 33, 33);">More details on the Special Session “Efficient and Resilient Machine Learning for Industrial Applications":
</span><span style="color: rgb(0, 120, 212);"><a href="https://www.esann.org/special-sessions#session6" class="ms-outlook-linkify" data-outlook-id="28e443f3-4a58-47c5-87bb-12cf945ab246" style="color: rgb(0, 120, 212);">https://www.esann.org/special-sessions#session6</a></span></div>
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Bruges, Belgium, 22-24 April 2026</div>
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****** Call For Papers ******</div>
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<span style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">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.</span></p>
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<span style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">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.</span></p>
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<span style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">Topics of interest include, but are not limited to:</span></p>
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Industrial challenges for AI</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
ML applications in industrial manufacturing</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Offline reinforcement learning</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Safe reinforcement learning</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Foundation models/GenAI for industry applications</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Benchmarking conventional methods and foundation models</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Explainable and trustworthy AI</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Data efficient simulation and forecasting</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
ML-assisted process modeling, simulation and monitoring</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Multimodal modeling and anomaly detection</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Modeling uncertainty in industrial settings</li><li style="font-family: Arimo, sans-serif; font-size: 12pt; color: rgb(51, 51, 51);">
Deep-learning-based time-series and signal processing</li></ul>
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****** Important Dates ******</div>
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Paper Submissions: 19 November 2025</div>
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Paper Acceptance Notifications: 23 January 2026</div>
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Conference: 22-24 April 2026</div>
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****** Session Organisers ******</div>
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Philipp Wissmann (Siemens AG, Germany)</div>
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Marc Weber (Siemens AG, Germany)</div>
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Simon Leszek (TU Berlin, Germany)</div>
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Philip Naumann (TU Berlin, Germany)</div>
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Daniel Hein (Siemens AG, Germany)</div>
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Steffen Udluft (Siemens AG, Germany)</div>
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Thomas Runkler (TU Munich, Germany)</div>
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