Recurrent Neural Networks for Missing or Asynchronous Data

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 di erent frequencies Unlike in the case of probabilistic models e g Gaussian of the missing variables the network does not attempt to model the distribution of the missing variables given the observed variables Instead it is a more discriminant approach that lls in the missing variables for the sole purpose of minimizing a learning criterion e g to minimize an output error