Paper
Learning distributed representations of concepts.
Concepts can be represented by distributed patterns of activity in networks of neuron-like units. One advantage of this kind of representation is that it leads to automatic generalization. When the weights in the network are changed to incorporate new knowledge about one concept, the changes affect the knowledge associated with other concepts that are represented by similar activity patterns. There have been numerous\ndemonstrations of sensible generalization which have depended on the experimenter choosing appropriately similar patterns for different concepts. This paper shows how the network can be made to choose the patterns itself when shown a set of propositions that use the concepts. It chooses patterns which make explicit the underlying features that are only implicit in the propositions it is shown.
Authors: Geoffrey E. Hinton