Overparameterized networks trained to convergence have shown impressive performance
in domains such as computer vision and natural language processing. Pushing state of the …

In this paper, we analyze two popular network compression techniques, ie filter pruning and
low-rank decomposition, in a unified sense. By simply changing the way the sparsity …

Abstract Systems medicine (SM) has emerged as a powerful tool for studying the human
body at the systems level with the aim of improving our understanding, prevention and …

In this paper, we propose a novel channel pruning method to reduce the computational and
storage costs of Convolutional Neural Networks (CNNs). Many existing one-shot pruning …

Deep networks have achieved impressive performance in various domains, but their
applications are largely limited by the prohibitive computational overhead. In this paper, we …

Recently, convolutional neural networks (CNNs) have set latest state-of-the-art on various
human activity recognition (HAR) datasets. However, deep CNNs often require more …

Abstract Ordered Weighted $ L_ {1} $(OWL) regularized regression is a new regression
analysis for high-dimensional sparse learning. Proximal gradient methods are used as …

Model compression methods have become popular in recent years, which aim to alleviate
the heavy load of deep neural networks (DNNs) in real-world applications. However, most of …

Channel pruning can significantly accelerate and compress deep neural networks. Many
channel pruning works utilize structured sparsity regularization to zero out all the weights in …

Data drift is a thorny challenge when deploying person re-identification (ReID) models into
real-world devices, where the data distribution is significantly different from that of the …