Paper:
Vanishing and exploding gradients are two of the main obstacles in training deep neural networks, especially in capturing long range dependencies in recurrent neural networks (RNNs). In this paper, we present an efficient parametrization of the transition matrix of an RNN that allows us to stabilize the gradients that arise in its training. Specifically, we parameterize the transition matrix by its singular value decomposition (SVD), which allows us to explicitly track and control its singular values. We attain efficiency by using tools that are common in numerical linear algebra, namely Householder reflectors for representing the orthogonal matrices that arise in the SVD. By explicitly controlling the singular values, our proposed svdRNN method allows us to easily solve the exploding gradient problem and we observe that it empirically solves the vanishing gradient issue to a large extent. We note that the SVD parameterization can be used for any rectangular weight matrix, hence it can be easily extended to any deep neural network, such as a multi-layer perceptron. Theoretically, we demonstrate that our parameterization does not lose any expressive power, and show how it potentially makes the optimization process easier. Our extensive experimental results also demonstrate that the proposed framework converges faster, and has good generalization, especially in capturing long range dependencies, as shown on the synthetic addition and copying tasks.

Review:
This paper suggests a reparametrization of the transition matrix. The proposed reparametrization which is based on Singular Value Decomposition can be used for both recurrent and feedforward networks.
The paper is well-written and authors explain related work adequately. The paper is a follow up on Unitary RNNs which suggest a reparametrization that forces the transition matrix to be unitary. The problem of vanishing and exploding gradient in deep network is very challenging and any work that shed lights on this problem can have a significant impact. 
I have two comments on the experiment section:
- Choice of experiments. Authors have chosen UCR datasets and MNIST for the experiments while other experiments are more common. For example, the adding problem, the copying problem and the permuted MNIST problem and language modeling are the common experiments in the context of RNNs. For feedforward settings, classification on CIFAR10 and CIFAR100 is often reported.
- Stopping condition. The plots suggest that the optimization has stopped earlier for some models. Is this because of some stopping condition or because of gradient explosion? Is there a way to avoid this?
- Quality of figures. Figures are very hard to read because of small font. Also, the captions need to describe more details about the figures.