Generating Sequences With Recurrent Neural Networks

4 Aug 2013  ·  Alex Graves ·

This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Language Modelling enwik8 LSTM (7 layers) Bit per Character (BPC) 1.67 # 41


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