Project: Flanders Artificial Intelligence Research program (FAIR) – second cycle - 2024
2024-01-01 – 2024-12-31
- Abstract
The Flanders AI Research Program is a strategic basic research program with a consortium of eleven partners: the five Flemish universities (KU Leuven, University of Ghent, University of Antwerp, University of Hasselt, Vrije Universiteit Brussel) and six research centers (imec, Flanders Make, VIB, VITO, Sirris and ILVO).
The program brings together 300+ researchers on new AI methods that can be used in innovative applications in health, industry, planet&energy and society. This way, the program contributes to a successful adoption of AI in Flanders. The ambition is for Flanders to occupy a strong international position in the field of strategic basic research in AI, and this within a strong and sustainable Flemish ecosystem.
Five focus research themes have been selected: responsible AI, human-centered AI, sustainable AI (energy-efficient and high-performance), productive and data-efficient AI (systems that require little data, which perform by combining data with domain knowledge and experience of experts) and resilient and high-performant AI (robust against changes in the environment). The description of the work packages and their research tasks defines the aspects within these themes that will be investigated in the program. The AI solutions are demonstrated in real-life use cases. These results not only demonstrate the effectiveness, but also inspire companies for adoption and researchers for further research.
The Flanders AI Research Program is part of the Flanders AI Policy Plan. More info: www.flandersairesearch.be
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A deep learning approach to perform defect classification of freeze-dried product
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Tell me a story! Narrative-driven XAI with Large Language Models
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- Journal Article
- A2
- open access
Embedding-based pair generation for contrastive representation learning in audio-visual surveillance data
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- Journal Article
- A1
- open access
Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli
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Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion
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Uncovering unknown dynamics in water resource recovery facilities with neural differential equations and Shapley value analysis
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Interpretable machine learning models for COPD ease of breathing estimation
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A novel Kriging-assisted multi-objective optimisation method considering infeasibility ratio under input uncertainty
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Exploring and learning structure : active inference approach in navigational agents
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Modelling the effect of base component properties and processing conditions on mixture products using probabilistic, knowledge-guided neural networks