NIPS preprint in neuroprose

[becker@ai.toronto.edu]

The following paper is available as becker.prediction.ps.Z in neuroprose:


    LEARNING TO MAKE COHERENT PREDICTIONS IN DOMAINS WITH DISCONTINUITIES
				       
		    Suzanna Becker and Geoffrey E. Hinton
	    Department of Computer Science, University of Toronto

 				ABSTRACT


     We have previously described an unsupervised learning procedure that
discovers spatially coherent properties of the world by maximizing the
information that parameters extracted from different parts of the sensory
input convey about some common underlying cause.  When given random dot
stereograms of curved surfaces, this procedure learns to extract surface depth
because that is the property that is coherent across space.  It also learns
how to interpolate the depth at one location from the depths at nearby
locations (Becker and Hinton, 1992).  In this paper, we propose two new models
which handle surfaces with discontinuities. The first model attempts to detect
cases of discontinuities and reject them.  The second model develops a mixture
of expert interpolators.  It learns to detect the locations of discontinuities
and to invoke specialized, asymmetric interpolators that do not cross the
discontinuities.