  Deep neural networks have been extremely successful at various image, speech,
video recognition tasks because of their ability to model deep structures
within the data. However, they are still prohibitively expensive to train and
apply for problems containing millions of classes in the output layer. Based on
the observation that the key computation common to most neural network layers
is a vector/matrix product, we propose a fast locality-sensitive hashing
technique to approximate the actual dot product enabling us to scale up the
training and inference to millions of output classes. We evaluate our technique
on three diverse large-scale recognition tasks and show that our approach can
train large-scale models at a faster rate (in terms of steps/total time)
compared to baseline methods.
