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Shedding Weights: More With Less Tsvi Achler, Cyrus Omar, and Eyal Amir

Summary: Shedding Weights: More With Less
Tsvi Achler, Cyrus Omar, and Eyal Amir
Abstract--Traditional connectionist classification models
place an emphasis on learned synaptic weights. Based on
neurobiological evidence, a new approach is developed and
experimentally shown to be more robust for disambiguating
novel combinations of stimuli. It requires less training,
variables and avoids many training related questions. Instead
of determining all connection weights a-priori based on the
training set, only positive binary associations are encoded (i.e.
X has Y). Negative associations (i.e. X does not have Z) are not
encoded, but inferred during the test phase through feedback
connections. This allows the network to function outside its
training distribution. For example, the network is able to
recognize multiple stimuli even if it is only trained on single
stimuli. We compare the accuracy and generalization of this
network with traditional weight learning networks.
UALITATIVELY intelligence is the ability to apply the
learned information to a new scenario. Classification is


Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign


Collections: Computer Technologies and Information Sciences