  A method for correcting for detector smearing effects using machine learning
techniques is presented. Compared to the standard approaches the method can use
more than one reconstructed variable to infere the value of the unsmeared
quantity on event by event basis. The method is implemented using a sequential
neural network with a categorical cross entropy as the loss function. It is
tested on a toy example and is shown to satisfy basic closure tests. Possible
application of the method for analysis of the data from high energy physics
experiments is discussed.
