Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing. (arXiv:2203.06031v1 [cs.LG])

This work focuses on designing low complexity hybrid tensor networks by
considering trade-offs between the model complexity and practical performance.
Firstly, we exploit a low-rank tensor-train deep neural network (TT-DNN) to
build an end-to-end deep learning pipeline, namely LR-TT-DNN. Secondly, a
hybrid model combining LR-TT-DNN with a convolutional neural network (CNN),
which is denoted as CNN+(LR-TT-DNN), is set up to boost the performance.
Instead of randomly assigning large TT-ranks for TT-DNN, we leverage Riemannian
gradient descent to determine a TT-DNN …

arxiv gradient networks neural networks processing speech speech processing