1 research outputs found
Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods
Advancements in parallel processing have lead to a surge in multilayer
perceptrons' (MLP) applications and deep learning in the past decades.
Recurrent Neural Networks (RNNs) give additional representational power to
feedforward MLPs by providing a way to treat sequential data. However, RNNs are
hard to train using conventional error backpropagation methods because of the
difficulty in relating inputs over many time-steps. Regularization approaches
from MLP sphere, like dropout and noisy weight training, have been
insufficiently applied and tested on simple RNNs. Moreover, solutions have been
proposed to improve convergence in RNNs but not enough to improve the long term
dependency remembering capabilities thereof.
In this study, we aim to empirically evaluate the remembering and
generalization ability of RNNs on polyphonic musical datasets. The models are
trained with injected noise, random dropout, norm-based regularizers and their
respective performances compared to well-initialized plain RNNs and advanced
regularization methods like fast-dropout. We conclude with evidence that
training with noise does not improve performance as conjectured by a few works
in RNN optimization before ours