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INFORMATION-THEORETIC LIMITS FOR DEEP NEURAL NETWORKS

Project description

Towards a greater understanding of deep neural networks

Deep neural networks (DNN) are based on a web of multiple units bound together by mathematical functions that allow for learning. In recent decades, these algorithms have assisted in computer vision, speech and audio recognition and natural language processing. A common criticism though remains that deep-learning algorithms are often used as black box, which is unsatisfactory in all applications for which performance guarantees are critical. The EU-funded IT-DNN project aims to enhance the understanding of DNNs. This will be done by developing novel information-theoretic bounds on the generalisation error attainable using DNN and by demonstrating how such bounds can guide the design of such networks.

Coordinator

CHALMERS TEKNISKA HOGSKOLA AB
Net EU contribution
€ 203 852,16
Address
-
412 96 Goteborg
Sweden

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Region
Södra Sverige Västsverige Västra Götalands län
Activity type
Higher or Secondary Education Establishments
Non-EU contribution
€ 0,00