Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization

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Abstract:

Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders for sequences, which allows to maximize the short-term memory and that can be solved with a closed-form solution without backpropagation. We introduce an initi...More

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