Modern Machine learning techniques take advantage of the exponentially rising calculation
power in new generation processor units. Thus, the number of parameters which are trained …

Neural networks are easier to optimise when they have many more weights than are
required for modelling the mapping from inputs to outputs. This suggests a two-stage …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Pruning is a popular technique for compressing a neural network: a large pre-trained
network is fine-tuned while connections are successively removed. However, the value of …

Neural network pruning techniques can reduce the parameter counts of trained networks by
over 90%, decreasing storage requirements and improving computational performance of …

Much work has been done recently to make neural networks more interpretable, and one
approach is to arrange for the network to use only a subset of the available features. In linear …

This paper describes a new method for pruning artificial neural networks, using a measure
of the neural complexity of the neural network. This measure is used to determine the …

The training of sparse neural networks is becoming an increasingly important tool for
reducing the computational footprint of models at training and evaluation, as well enabling …

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their
accuracy does not degrade when the network width is increased. Recent evidence suggests …

Through the success of deep learning in various domains, artificial neural networks are
currently among the most used artificial intelligence methods. Taking inspiration from the …