Deep Convolutional Networks are Hierarchical Kernel Machines
We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, respectively. Under the assumption of normalized inputs, we show that appropriat...
| Main Authors: | , , , |
|---|---|
| Format: | Technical Report |
| Language: | en_US |
| Published: |
Center for Brains, Minds and Machines (CBMM), arXiv
2015
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/100200 |