Search
Now showing items 11-14 of 14
Deep Convolutional Networks are Hierarchical Kernel Machines
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-08-05)
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, ...
Learning An Invariant Speech Representation
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-15)
Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the acoustic input. We formulate the problem of ...
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex
(Center for Brains, Minds and Machines (CBMM), bioRxiv, 2015-04-26)
Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to ...
Parsing Occluded People by Flexible Compositions
(Center for Brains, Minds and Machines (CBMM), arXiv, 2015-06-01)
This paper presents an approach to parsing humans when there is significant occlusion. We model humans using a graphical model which has a tree structure building on recent work [32, 6] and exploit the connectivity prior ...