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...
| Principais autores: | , , |
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| Formato: | Technical Report |
| Idioma: | en_US |
| Publicado em: |
Center for Brains, Minds and Machines (CBMM), arXiv
2016
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| Assuntos: | |
| Acesso em linha: | http://hdl.handle.net/1721.1/101635 |