Brain style computation: learning and generalization

By David E. Rumelhart. 1990

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Citations in the corpus

Generalization Through the Recurrent Interaction of Episodic Memories (2012, methodology): “…using error correction algorithms such as backpropagation, are indeed powerful learners of the statistical structure of data sets, for instance comprised of semantic facts (e.g., a robin can fly; McClelland et al., 2010, 1995; McClelland & Rogers, 2003; Rogers & McClelland, 2004; Rumelhart, 1990).”
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. (1995, methodology): “Rumelhart (1990) trained a network on propositions about a number of concepts: living things, plants, animals, trees, oaks, pines, flowers, roses, daisies, animals, birds, canaries, robins, fish, salmon, and sunfish.”
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated. (2016, unknown): “…knowledge representation in the connection weights among the units in the network, shaped by the statistics of the environment in a fashion that was ef ﬁ cient and generalized to novel examples [1,14,15], while also supporting performance on atypical items occurring frequently in the domain.”
Précis of Semantic Cognition: A Parallel Distributed Processing Approach (2008, methodology): “As previously mentioned, the models we will use to illustrate the theory are variants of an architecture first proposed by Rumelhart (Rumelhart 1990; Rumelhart & Todd 1993) as illustrated in Figure 1.”