|Title||Theory I: Deep networks and the curse of dimensionality|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Poggio, T, Liao, Q|
|Journal||Bulletin of the Polish Academy of Sciences: Technical Sciences|
|Keywords||convolutional neural networks, deep and shallow networks, deep learning, function approximation|
We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
- CBMM Funded