neural-network-compression

This paper proposes a controllability–observability framework for compressing deep neural networks by reducing hidden-state redundancy, demonstrating significant compression with minimal accuracy loss on MNIST and CIFAR-10.

Introduces Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for compressing deep neural network layers via small core tensors, achieving high compression ratios while maintaining accuracy.