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We present a learning algorithm for the recurrent random network model (Gelenbe 89,90) using gradient descent of a quadratic error function.
Learning in the Recurrent Random Neural Network. Abstract: The capacity to learn from examples is one of the most desirable features of neural network models.
Jan 1, 1993 · We present a learning algorithm for the recurrent random network model (Gelenbe 1989, 1990) using gradient descent of a quadratic error function ...
Aug 15, 2024 · We present a learning algorithm for the recurrent random network model [9,10] using gradient descent of a quadratic error function. The ...
1986). Designing effective learning algorithms for general (i.e., recurrent) networks is a current and legitimate scientific concern in neural network theory.
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We present a learning algorithm for the recurrent random network model (Gelenbe 89,90) using gradient descent of a quadratic error function. The analytical ...
We present a learning algorithm for the recurrent random network model (Gelenbe 1989, 1990) using gradient descent of a quadratic error function.
Nov 23, 2009 · Feedforward multi-layer neural networks with backprop are used with Reinforcement Learning as to help it generalize the actions our agent does.
Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory.
May 14, 2024 · These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs.