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Cascade Processes for Sparse Machine Learning

CORDIS · observation · Publication date unknown

Cascade Processes for Sparse Machine Learning Deep learning continues to achieve impressive breakthroughs across disciplines and is a major driving force behind a multitude of industry innovations. Most of its successes are achieved by increasingly large neural networks that are trained on massive data sets. Their development inflicts costs that are only affordable by a few labs and prevent global participation in the creation of related technologies. The huge model sizes also pose computational challenges for algorithms that aim to address issues with features that are critical in real-world applications like fairness, adversarial robustness, and interpretability. The high demand of neural networks for vast amounts of data further limits their utility for solving highly relevant tasks in biomedicine, economics, or natural sciences. To democratize deep learning and to broaden its applicability, we have to find ways

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award

status

SIGNED

region

EU

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1499285

unit

EUR