SOURCE-LINKED INTELLIGENCE

Correlations-Oriented Representation Learning

CORDIS · observation · Publication date unknown

Correlations-Oriented Representation Learning Deep learning methods have propelled many of the recent remarkable achievements of artificial intelligence, yet their inner workings remain enigmatic. How do these methods represent the training examples in their multiple processing layers? How are these representations used to make decisions on previously unseen data? The Correlation-Oriented Representation Learning (CORaL) project delves into the intricate relationship between data correlations and the hidden representations of deep neural networks. CORaL's core objectives encompass: i) characterizing the structure of data correlations in tasks where deep learning methods excel; ii) providing a theoretical description of the interplay between the learning dynamics of deep networks and data correlations; and iii) validating these theoretical insights on benchmark machine-learning datasets. The Researcher will approach these objectives w

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recordType

award

status

SIGNED

region

EU

value

172750.08

unit

EUR