  Weight initialization is important for faster convergence and stability of
deep neural networks training. In this paper, a robust initialization method is
developed to address the training instability in long short-term memory (LSTM)
networks. It is based on a normalized random initialization of the network
weights that aims at preserving the variance of the network input and output in
the same range. The method is applied to standard LSTMs for univariate time
series regression and to LSTMs robust to missing values for multivariate
disease progression modeling. The results show that in all cases, the proposed
initialization method outperforms the state-of-the-art initialization
techniques in terms of training convergence and generalization performance of
the obtained solution.
