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Abstract:
In many real world tasks, only a small fraction of the
available inputs are important at any particular time. This paper
presents a method for ascertaining the relevance of inputs by
exploiting temporal coherence and predictability. The method
proposed in this paper dynamically allocates relevance to inputs by
using expectations of their future values. As a model of the task
is learned, the model is simultaneously extended to create
task-specific predictions of the future values of inputs. Inputs
which are either not relevant, and therefore not accounted for in
the model, or those which contain noise, will not be predicted
accurately. These inputs can be de-emphasized, and, in turn, a new,
improved, model of the task created. The techniques presented in
this paper have yielded significant improvements for the
vision-based autonomous control of a land vehicle, vision-based
hand tracking in cluttered scenes, and the detection of faults in
the etching of semiconductor wafers.
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