Now showing items 1-5 of 5
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 ...
Computational role of eccentricity dependent cortical magnification
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-06)
We develop a sampling extension of M-theory focused on invariance to scale and translation. Quite surprisingly, the theory predicts an architecture of early vision with increasing receptive field sizes and a high resolution ...
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 ...
A Deep Representation for Invariance And Music Classification
(Center for Brains, Minds and Machines (CBMM), arXiv, 2014-17-03)
Representations in the auditory cortex might be based on mechanisms similar to the visual ventral stream; modules for building invariance to transformations and multiple layers for compositionality and selectivity. In this ...
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, ...