Transcription of Generating Sequences With Recurrent Neural Networks
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Generating Sequences with Recurrent Neural Networks Alex Graves [ ] 5 Jun 2014. Department of Computer Science University of Toronto Abstract This paper shows how Long Short-term Memory Recurrent Neural net- works can be used to generate complex Sequences with long-range struc- ture, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwrit- ing (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence . The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.
1;:::;x T) is passed through weighted connections to a stack of N recurrently connected hidden layers to compute rst the hidden vector sequences h n= (h 1;:::;h n T) and then the output vector sequence y = (y 1;:::;y T). Each output vector y t is used to parameterise a predictive distribution Pr(x t+1jy t) over the possible next inputs x t+1 ...
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