Recurrent Neural Networks for Missing or Asynchronous Data

Bengio, Yoshua, Gingras, Francois

Neural Information Processing Systems 

In this paper we propose recurrent neural networks with feedback into the input units for handling two types of data analysis problems. On the one hand, this scheme can be used for static data when some of the input variables are missing. On the other hand, it can also be used for sequential data, when some of the input variables are missing or are available at different frequencies.

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