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Advancements in parallel processing have lead to a surge in multilayerperceptrons' (MLP) applications and deep learning in the past decades.Recurrent Neural Networks (RNNs) give additional representational power tofeedforward MLPs by providing a way to treat sequential data. However, RNNs arehard to train using conventional error backpropagation methods because of thedifficulty in relating inputs over many time-steps. Regularization approachesfrom MLP sphere, like dropout and noisy weight training, have beeninsufficiently applied and tested on simple RNNs. Moreover, solutions have beenproposed to improve convergence in RNNs but not enough to improve the long termdependency remembering capabilities thereof.  In this study, we aim to empirically evaluate the remembering andgeneralization ability of RNNs on polyphonic musical datasets. The models aretrained with injected noise, random dropout, norm-based regularizers and theirrespective performances compared to well-initialized plain RNNs and advancedregularization methods like fast-dropout. We conclude with evidence thattraining with noise does not improve performance as conjectured by a few worksin RNN optimization before ours.

Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods