> I thought the main selling point of deep learning that it finds non-linear connections in the data.
Agreed. Yet there are unknown feature spaces in which the deep nets separate the data linearly; the mapping learnt by the deep nets from the data to this space is likely highly nonlinear.
For example, a binary logistic regression can be thought as mapping data to a 1D space ([0, 1]), and separates the two classes at say 1/2 linearly.
Ok thanks for the example, so what you're saying is that this research tries to explain how it separates the data linearly starting from this unknown feature space?
Agreed. Yet there are unknown feature spaces in which the deep nets separate the data linearly; the mapping learnt by the deep nets from the data to this space is likely highly nonlinear.
For example, a binary logistic regression can be thought as mapping data to a 1D space ([0, 1]), and separates the two classes at say 1/2 linearly.