%0 Generic %1 vidal2017mathematics %A Vidal, Rene %A Bruna, Joan %A Giryes, Raja %A Soatto, Stefano %D 2017 %K 2017 arxiv deep-learning mathematics %T Mathematics of Deep Learning %U http://arxiv.org/abs/1712.04741 %X 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.

@misc{vidal2017mathematics, 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.}, added-at = {2018-03-24T15:12:21.000+0100}, author = {Vidal, Rene and Bruna, Joan and Giryes, Raja and Soatto, Stefano}, biburl = {https://www.bibsonomy.org/bibtex/2b17ab2d38a9970cc9627eacb207650b6/achakraborty}, description = {[1712.04741] Mathematics of Deep Learning}, interhash = {2b3660fcac77806f2d488d55a965e20c}, intrahash = {b17ab2d38a9970cc9627eacb207650b6}, keywords = {2017 arxiv deep-learning mathematics}, note = {cite arxiv:1712.04741}, timestamp = {2018-03-24T15:12:21.000+0100}, title = {Mathematics of Deep Learning}, url = {http://arxiv.org/abs/1712.04741}, year = 2017 }