Jakubovitz, D;
Giryes, R;
Rodrigues, MRD;
(2019)
Generalization Error in Deep Learning.
In: Boche, H and Caire, G and Calderbank, R and Kutyniok, G and Mathar, R and Petersen, P, (eds.)
Compressed Sensing and Its Applications.
(pp. pp. 153-193).
Birkhäuser Boston: Berlin, Germany.
Preview |
Text
1808.01174.pdf - Accepted Version Download (540kB) | Preview |
Abstract
Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this chapter, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results.
| Type: | Proceedings paper |
|---|---|
| Title: | Generalization Error in Deep Learning |
| Event: | Third International MATHEON Conference 2017 |
| Location: | Tech Univ Berlin, Berlin, GERMANY |
| Dates: | 04 December 2017 - 08 December 2017 |
| ISBN-13: | 978-3-319-73073-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-319-73074-5_5 |
| Publisher version: | https://doi.org/10.1007/978-3-319-73074-5_5 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10086807 |
Archive Staff Only
 |
View Item |
