  After a more than decade-long period of relatively little research activity
in the area of recurrent neural networks, several new developments will be
reviewed here that have allowed substantial progress both in understanding and
in technical solutions towards more efficient training of recurrent networks.
These advances have been motivated by and related to the optimization issues
surrounding deep learning. Although recurrent networks are extremely powerful
in what they can in principle represent in terms of modelling sequences,their
training is plagued by two aspects of the same issue regarding the learning of
long-term dependencies. Experiments reported here evaluate the use of clipping
gradients, spanning longer time ranges with leaky integration, advanced
momentum techniques, using more powerful output probability models, and
encouraging sparser gradients to help symmetry breaking and credit assignment.
The experiments are performed on text and music data and show off the combined
effects of these techniques in generally improving both training and test
error.
