Abstract
A deep network structure is formed with LSTM layer and convolutional layer interweaves with each other. The Layerwise Interweaving Convolutional LSTM (LIC-LSTM) enhanced the feature extraction ability of LSTM stack and is capable for versatile sequential data modeling. Its unique network structure allows it to extract higher level features with sequential information involved. Experiment results show the model achieves higher accuracy and shoulders lower perplexity on sequential data modeling tasks compared with state of art LSTM models.
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Duan, T., Srihari, S.N. (2017). Layerwise Interweaving Convolutional LSTM. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_31
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DOI: https://doi.org/10.1007/978-3-319-57351-9_31
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