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arXiv · 1612.04035

DizzyRNN: Reparameterizing Recurrent Neural Networks for Norm-Preserving Backpropagation

Victor Dorobantu·Per Andre Stromhaug·Jess Renteria
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Abstract

The vanishing and exploding gradient problems are well-studied obstacles that make it difficult for recurrent neural networks to learn long-term time dependencies. We propose a reparameterization of standard recurrent neural networks to update linear transformations in a provably norm-preserving way through Givens rotations. Additionally, we use the absolute value function as an element-wise non-linearity to preserve the norm of backpropagated signals over the entire network. We show that this reparameterization reduces the number of parameters and maintains the same algorithmic complexity as a standard recurrent neural network, while outperforming standard recurrent neural networks with orthogonal initializations and Long Short-Term Memory networks on the copy problem.

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Victor Dorobantu, Per Andre Stromhaug, Jess Renteria. 2016-12-13. DizzyRNN: Reparameterizing Recurrent Neural Networks for Norm-Preserving Backpropagation. https://arxiv.org/abs/1612.04035

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