Most state-of-the-art deep neural networks are overparameterized and exhibit a high
computational cost. A straightforward approach to this problem is to replace convolutional …

Memory footprint is one of the main limiting factors for large neural network training. In
backpropagation, one needs to store the input to each operation in the computational graph …

A rising problem in the compression of Deep Neural Networks is how to reduce the number
of parameters in convolutional kernels and the complexity of these layers by low-rank tensor …

Tensor decomposition methods have proven effective in various applications, including
compression and acceleration of neural networks. At the same time, the problem of …

Counting the number of people in a specific area is crucial in maintaining proper crowd
safety and management especially in highly-congested indoor scenarios. Recent …

Compressing deep neural networks is of great importance for real-world applications on
resourceconstrained devices. Tensor decomposition is one promising answer that retains …