Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 13th June 2026, 02:18:22pm CEST
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Daily Overview |
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Session 1a: Benchmarking & Testing
Session Topics: Agentic AI, Foundation Models, Generative Models, Graph Neural Networks, Physics-informed Machine Learning, Reinforcement Learning, Probabilistic Methods, Uncertainty Quantification, Audio, Other, Graphs, Image, Multimodal Data, Simulation Data, Tabular Data, Text, Time Series, Video, Other, Core Machine Learning, Aeronautics, Space & Transport, Energy, Earth & Environment, Health, Information, Matter
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Choose from expert-led talks running simultaneously to explore AI topics that match your interests. | ||
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2:00pm - 2:20pm
Invited talk ID: 402 / a Tue | LAB 14h Parallel S 1a: 001 Modalities: Graphs, Multimodal Data Methods: Physics-informed Machine Learning Application Domain: Core Machine Learning Embracing the Tyranny of Testing Max Planck Institute for Intelligent Systems, Germany 2:20pm - 2:34pm
ID: 246 / a Tue | LAB 14h Parallel S 1a: 002 Modalities: Image, Video Methods: Other Application Domain: Core Machine Learning Bridging Perception and Logic: An Abductive Learning Cycle for Semantically Anchored Facial Expression Recognition 1Neu-Ulm University of Applied Sciences, Germany; 2University of Würzburg 2:34pm - 2:48pm
ID: 356 / a Tue | LAB 14h Parallel S 1a: 003 Modalities: Image Methods: Foundation Models Application Domain: Core Machine Learning Human-in-the-loop Concept Discovery and Curation in Vision Foundation Models 1Helmholtz Munich, Germany; 2KAIST AI, South Korea 2:48pm - 3:02pm
ID: 174 / a Tue | LAB 14h Parallel S 1a: 004 Modalities: Image, Multimodal Data, Tabular Data Methods: Foundation Models Application Domain: Health TACTIC: Tabular-Attribute Conditioned Transformer for Image Classification 1Helmholtz Munich, Germany; 2Technical University of Munich, Germany; 3King's College London, UK 3:02pm - 3:15pm
ID: 369 / a Tue | LAB 14h Parallel S 1a: 005 Modalities: Graphs, Simulation Data, Tabular Data Methods: Generative Models, Probabilistic Methods, Uncertainty Quantification, Other Application Domain: Core Machine Learning, Matter Learning Physical Geometry from Noisy Helical Particle Tracks: A Comparative Study of Transformers, SBI, and JEPA L2I Toulouse, CNRS/IN2P3, Université de Toulouse | ||
