Special Session Organized by
Christoph-Alexander Holst, inIT – Institute Industrial IT, Germany, Volker Lohweg, inIT – Institute Industrial IT, Germany, Rui Pinto, Faculdade de Engenharia da Universidade do Porto, Portugal Simona Salicone, Politecnico di Milano, Italy, and Pedro Torres,Instituto Politécnico de Castelo Branco, Portugal.
Download Call for Papers
Click here to download the special session cfp.
Focus
Machine learning often relies on large datasets, but real-world applications frequently face data scarcity due to limited sensor capabilities, expensive labeling, or rare phenomena such as machine faults. The goal in dealing with scarce data must be to obtain as much information as possible from the available data. Overcoming data scarcity is not only a technical challenge but also a step towards sustainable AI, enabling the development of effective models with fewer resources and minimising energy-intensive data collection and labeling processes. Methods to address data scarcity include data efficient algorithms, data augmentation, transfer learning, and information fusion that explicitly model and manage uncertainty.