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Abstract - Technical Program - ES 99
Learning in Intelligent Embedded Systems
Daniel D. Lee, Lucent Technologies--Bell Laboratories, and H. Sebastian Seung, Massachusetts Institute of Technology
Abstract
Information processing capabilities of embedded systems presently lack
the robustness and rich complexity found in biological systems.
Endowing artificial systems with the ability to adapt to changing
conditions requires algorithms that can rapidly learn from examples.
We demonstrate the application of one such learning algorithm on an
inexpensive robot constructed to perform simple sensorimotor tasks.
The robot learns to track a particular object by discovering the
salient visual and auditory cues unique to that object. The system
uses a convolutional neural network to combine color, luminance,
motion, and auditory information. The weights of the networks are
adjusted using feedback from a teacher to reflect the reliability of
the various input channels in the surrounding environment. We also
discuss how unsupervised learning can discover features in data
without external interaction. An unsupervised algorithm based upon
nonnegative matrix factorization is able to automatically learn the
different parts of objects. Such a parts-based representation of data
is crucial for robust object recognition.
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Technical Program