Machine Learning

Techniques to Handle Very Long Sequences with LSTMs

Last Updated on August 14, 2019

Long Short-Term Memory or LSTM recurrent neural networks are capable of learning and remembering over long sequences of inputs.

LSTMs work very well if your problem has one output for every input, like time series forecasting or text translation. But LSTMs can be challenging to use when you have very long input sequences and only one or a handful of outputs.

This is often called sequence labeling, or sequence classification.

Some examples include:

  • Classification of sentiment in documents containing thousands of words (natural language processing).
  • Classification of an EEG trace of thousands of time steps (medicine).
  • Classification of coding or non-coding genes for sequences of thousands of DNA base pairs (bioinformatics).

These so-called sequence classification tasks require special handling when using recurrent neural networks, like LSTMs.

In this post, you will discover 6 ways to handle very long sequences for sequence classification problems.

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