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Recurrent neural networks are capable of learning context-free tasks, however learning performance is unsatisfactory. We investigate the eeect of biasing learning towards nding a solution to a context-free prediction task. The rst series of simulations xes various sets of weights of the network to values found in a successful network, limiting the search(More)
Recurrent neural network processing of regular language is reasonably well understood. Recent work has examined the less familiar question of context-free languages. Previous results regarding the language a n b n suggest that while it is possible for a small recurrent network to process context-free languages, learning them is difficult. This paper(More)
In recent years it has been shown that first order recurrent neural networks trained by gradient-descent can learn not only regular but also simple context-free and context-sensitive languages. However, the success rate was generally low and severe instability issues were encountered. The present study examines the hypothesis that a combination of(More)
We examined the interrelations of outcome, time elapsed during cardiopulmonary resuscitation (CPR), and blood glucose levels drawn from 83 patients with out-of-hospital cardiac arrest. Levels rose significantly during CPR. Although slope and intercept of regression lines differed for those dying in the field and those admitted, regression lines were similar(More)