Cyclic models and recurrent neural networks
Keyword(s):
This chapter discusses models with cyclic dependencies. There are two principle architectures that are discussed. The first principle architecture of cyclic graphs comprises directed graphs similar to the Bayesian networks except that they include loops. Formally, such networks represent dynamical systems in the wider context and therefore represent some form of temporal modeling. The second type of models have connections between neurons that are bi-directional. These types of networks will be discussed in the context of stochastic units in the second half of this chapter.
2000 ◽
Vol 31
(4)
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pp. 77-86
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2000 ◽
Vol 47
(4)
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pp. 575-578
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1998 ◽
pp. 171-219
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