Mathematics of Deep Learning
Machine Learning
2017-12-14 v1 Computer Vision and Pattern Recognition
Abstract
Recently there has been a dramatic increase in the performance of recognition systems due to the introduction of deep architectures for representation learning and classification. However, the mathematical reasons for this success remain elusive. This tutorial will review recent work that aims to provide a mathematical justification for several properties of deep networks, such as global optimality, geometric stability, and invariance of the learned representations.
Cite
@article{arxiv.1712.04741,
title = {Mathematics of Deep Learning},
author = {Rene Vidal and Joan Bruna and Raja Giryes and Stefano Soatto},
journal= {arXiv preprint arXiv:1712.04741},
year = {2017}
}