INNER: information theory of deep neural networks

Over the last decade, deep-learning algorithms have dramatically improved the state of the art in many machine-learning problems, including computer vision, speech recognition, natural language processing, and audio recognition. Despite their success, however, there is no satisfactory mathematical theory that explains the functioning of such algorithms. Indeed, a common critique is that deep-learning algorithms are often used as black box, which is unsatisfactory in all applications for which performance guarantees are critical (e.g., traffic-safety applications).

The purpose of this project is to increase our theoretical understanding of deep neural networks. This will be done by relying on tools of information theory and focusing on specific tasks that are relevant to computer vision.

Partner organizations

  • Chalmers AI Research Centre (CHAIR) (Centre, Sweden)
Start date 01/01/2019
End date The project is closed: 31/12/2021

Page manager Published: Tue 24 Sep 2019.