Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …

Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …

Abstract As Deep Neural Networks (DNNs) usually are overparameterized and have millions
of weight parameters, it is challenging to deploy these large DNN models on resource …

Today's deep neural networks (DNNs) are becoming deeper and wider because of
increasing demand on the analysis quality and more and more complex applications to …

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 …

Deep networks often possess a vast number of parameters, and their significant redundancy
in parameterization has become a widely-recognized property. This presents significant …

Abstract Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is
an increased demand for deploying DNNs on devices that do not have abundant resources …

The success of deep learning in numerous application domains created the de-sire to run
and train them on mobile devices. This however, conflicts with their computationally, memory …

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

Layer-wise magnitude-based pruning (LMP) is a very popular method for deep neural
network (DNN) compression. However, tuning the layerspecific thresholds is a difficult task …