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Reconciling Classical and Modern (Deep) Machine Learning for Real-World Applications

CORDIS · observation · Publication date unknown

Reconciling Classical and Modern (Deep) Machine Learning for Real-World Applications Despite the undeniable success of machine learning in addressing a wide variety of technological and scientific challenges, the current trend of training predictive models with an evergrowing number of parameters from an evergrowing amount of data is not sustainable. These huge models, often engineered by large corporations benefiting from huge computational resources, typically require learning a billion or more of parameters. They have proven to be very effective in solving prediction tasks in computer vision, natural language processing, and computational biology, for example, but they mostly remain black boxes that are hard to interpret, computationally demanding, and not robust to small data perturbations. With a strong emphasis on visual modeling, the grand challenge of APHELEIA is to develop a new generation of machine learning models t

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award

status

SIGNED

region

EU

value

1999375

unit

EUR