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| Titel: |
Learning Recursive Distributed Representations for Holistic Computation |
| Auteur: |
Chrisman, Lonnie |
| Verschenen in: |
Connection science |
| Paginering: | Jaargang 3 (1991) nr. 4 pagina's 345-366 |
| Jaar: |
1991 |
| Inhoud: |
A number of connectionist models capable of representing data with compositional structure have recently appeared. These new models suggest the intriguing possibility of performing holistic structure-sensitive computations with distributed representations. Two possible forms of holistic inference, transformational inference and confluent inference, are identified and compared. Transformational inference was successfully demonstrated by Chalmers; however, the pure transformational approach does not consider the eventual inference tasks during the process of learning its representations. Confluent inference is introduced as a method for achieving a tight coupling between the distributed representations of a problem and the solution for the given inference task while the net is still learning its representations. A dual-ported RAAM architecture based on Pollack's Recursive Auto-Associative Memory is implemented and demonstrated in the domain of Natural Language translation. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 5 van 8 gevonden artikelen
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