| 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 |
| Volume | 66 |
| Issue | 6 |
| Keywords | convolutional neural networks, deep and shallow networks, deep learning, function approximation |
| Abstract | 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. |
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