Learning Real and Boolean Functions: When Is Deep Better Than Shallow
We describe computational tasks - especially in vision - that correspond to compositional/hierarchical functions. While the universal approximation property holds both for hierarchical and shallow networks, we prove that deep (hierarchical) networks can approximate the class of compositional functio...
| Main Authors: | , , |
|---|---|
| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM), arXiv
2016
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/101635 |