  Importance sampling (IS) as an elegant and efficient variance reduction (VR)
technique for the acceleration of stochastic optimization problems has
attracted many researches recently. Unlike commonly adopted stochastic uniform
sampling in stochastic optimizations, IS-integrated algorithms sample training
data at each iteration with respect to a weighted sampling probability
distribution $P$, which is constructed according to the precomputed importance
factors. Previous experimental results show that IS has achieved remarkable
progresses in the acceleration of training convergence. Unfortunately, the
calculation of the sampling probability distribution $P$ causes a major
limitation of IS: it requires the input data to be well-structured, i.e., the
feature vector is properly defined. Consequently, recurrent neural networks
(RNN) as a popular learning algorithm is not able to enjoy the benefits of IS
due to the fact that its raw input data, i.e., the training sequences, are
often unstructured which makes calculation of $P$ impossible. In considering of
the the popularity of RNN-based learning applications and their relative long
training time, we are interested in accelerating them through IS. This paper
propose a novel Fast-Importance-Mining algorithm to calculate the importance
factor for unstructured data which makes the application of IS in RNN-based
applications possible. Our experimental evaluation on popular open-source
RNN-based learning applications validate the effectiveness of IS in improving
the convergence rate of RNNs.
