Lazy Evaluation of Convolutional Filters
  In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network. This allows to trade-off the accuracy of a deep neural network with the computational and memory requirements. This is especially important on a constrained device unable to hold all the weights of the network in memory. 
Sam Leroux, Steven Bohez, Cedric De Boom, Elias De Coninck, Tim   Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
cs.CV cs.NE