Brown University / Google
Learning Invariant Features Using Inertial Priors, or "Why Google might want to be in the neocortex business?"
Tuesday 28th of November 2006 at 12:00pm
We address the technical challenges involved in combining key
features from several theories of the visual cortex in a single
computational model. The resulting model is a hierarchical Bayesian
network factored into modular component networks implementing
variable-order Markov models. Each component network has an
associated receptive field corresponding to components in the level
directly below it in the hierarchy. The variable-order Markov
models account for features that are invariant to naturally
occurring transformations in their inputs. These invariant features
support efficient generalization and produce increasingly stable,
persistent representations as we ascend the hierarchy. The
receptive fields of proximate components on the same level overlap
to restore selectivity that might otherwise be lost to invariance.
Technical jargon aside, we believe there is enough known about the
primate cortex to enable engineers to build systems that approach
the pattern-recognition capability of human vision. Moreover, we
believe that such a capability can be implemented using the
distributed computing infrastructure that Google has today.
3105 Tolman Hall (Beach Room)
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