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We propose a simplified architecture for a recurrent neural network designed for learning from structures. We describe the architecture and the implementation and show the performances of the net. Two examples from the science domain are discussed: the first uses a synthetic data set and the second illustrates a chemical problem. We discuss about the results and compare them to other applications,...
An algorithm called bidirectional long short-term memory networks (BLSTM) for processing sequential data is introduced. This supervised learning method trains a special recurrent neural network to use very long-range symmetric sequence context using a combination of nonlinear processing elements and linear feedback loops for storing long-range context. The algorithm is applied to the sequence-based...
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