Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks

ICLR 2019 Jun QiChin-Hui LeeJavier Tejedor

The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs). However, high Tensor-Train ranks for all the core tensors of parameters need to be element-wise fixed, which results in an unnecessary redundancy of model parameters... (read more)

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