Name
Abstract | ||
|---|---|---|
Many different discrete--time recurrent neural network architectureshave been proposed. However, there has been virtually noeffort to compare these architectures experimentally. In this paperwe review and categorize many of these architectures and comparehow they perform on various classes of simple problems includinggrammatical inference and nonlinear system identification.1 IntroductionIn the past few years several recurrent neural network architectures have emerged.In this paper we... |
Year | Venue | Keywords |
|---|---|---|
1994 | NIPS | recurrent neural network,discrete time,nonlinear system identification |
Field | DocType | Citations |
Categorization,Grammar induction,Computer science,Recurrent neural network,Nonlinear system identification,Time delay neural network,Artificial intelligence,Machine learning | Conference | 43 |
PageRank | References | Authors |
3.70 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bill G. Horne | 1 | 334 | 33.87 |
| C. Lee Giles | 2 | 11154 | 1549.48 |