Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

18 Feb 2016Gil KerenBjörn Schuller

Traditional convolutional layers extract features from patches of data by applying a non-linearity on an affine function of the input. We propose a model that enhances this feature extraction process for the case of sequential data, by feeding patches of the data into a recurrent neural network and using the outputs or hidden states of the recurrent units to compute the extracted features... (read more)

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