The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
Lorenzo Rosasco;
2017-01-01
Abstract
The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.File in questo prodotto:
File | Dimensione | Formato | |
---|---|---|---|
11567-888539.pdf
accesso aperto
Descrizione: Articolo principale
Tipologia:
Documento in versione editoriale
Dimensione
1.72 MB
Formato
Adobe PDF
|
1.72 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.