The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

As their size increases, Large Languages Models (LLMs) are natural candidates for network
pruning methods: approaches that drop a subset of network weights while striving to …

Neural network pruning---the task of reducing the size of a network by removing parameters--
-has been the subject of a great deal of work in recent years. We provide a meta-analysis of …

Abstract Knowledge distillation is a widely applicable technique for training a student neural
network under the guidance of a trained teacher network. For example, in neural network …

Previous works utilized" smaller-norm-less-important" criterion to prune filters with smaller
norm values in a convolutional neural network. In this paper, we analyze this norm-based …

We propose a variational Bayesian scheme for pruning convolutional neural networks in
channel level. This idea is motivated by the fact that deterministic value based pruning …

Filter pruning has been widely applied to neural network compression and acceleration.
Existing methods usually utilize pre-defined pruning criteria, such as Lp-norm, to prune …

This paper presents a survey of methods for pruning deep neural networks. It begins by
categorising over 150 studies based on the underlying approach used and then focuses on …

Channel pruning and tensor decomposition have received extensive attention in
convolutional neural network compression. However, these two techniques are traditionally …

Existing pruning techniques preserve deep neural networks' overall ability to make correct
predictions but could also amplify hidden biases during the compression process. We …