Outputs (4)

Methods for pruning deep neural networks (2022)
Journal Article

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 three categories:
methods that use magnitude based pruning, methods that... Read More about Methods for pruning deep neural networks.

Pruning neural networks using multi-armed bandits (2019)
Journal Article

The successful application of deep learning has led to increasing expectations
of their use in embedded systems. This in turn, has created the need to find
ways of reducing the size of neural networks. Decreasing the size of a neural
network requi... Read More about Pruning neural networks using multi-armed bandits.

A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images (2017)
Journal Article

Sign language is used by approximately 70 million1 people throughout the world, and an automatic tool for interpreting it could make a major impact on communication between those who use it and those who may not understand it.
However, computer inte... Read More about A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images.

Optimizing deep learning networks using multi-armed bandits
Thesis
Ameen, S. (in press). Optimizing deep learning networks using multi-armed bandits. (Thesis). The University of Salford

Deep learning has gained significant attention recently following their successful use for applications such as computer vision, speech recognition, and natural language processing. These deep learning models are based on very large neural networks,... Read More about Optimizing deep learning networks using multi-armed bandits.