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This paper provides a systematic analysis of the recurrent backpropagation (RBP) algorithm, introducing a number of new results. The main limitation of the RBP ...
Fixed Point Analysis for Recurrent Networks - NIPS papers
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The main limitation of the RBP algorithm is that it assumes the convergence of the network to a stable fixed point in order to backpropagate the error signals.
Dec 4, 2015 · This work analyzes the fixed-point performance of recurrent neural networks using a retrain based quantization method. The quantization ...
The author shows the existence of a fixed point for every recurrent neural network and uses a geometric approach to locate where the fixed points are.
Jul 27, 2023 · In computational neuroscience, fixed points of recurrent neural networks are commonly used to model neural responses to static or slowly ...
Jul 19, 2024 · We use a reparameterization of the recurrent network model to derive two alternative learning rules that produce more robust learning dynamics.
The backpropagation algorithm can be used for both recognition and generation of time trajectories. When used as a recognizer, it has been shown that the ...
This work analyzes the fixed-point performance of recurrent neural networks using a retrain based quantization method. The quantization sensitivity of each ...
Jan 1, 1988 · This paper provides a systematic analysis of the recurrent backpropagation (RBP) algorithm, introducing a number of new results.
We derive a structurally simple learning algorithm for recurrent networks which does not involve computing the trajectories of the system and we prove ...
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