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All Outputs (3)

Methods for pruning deep neural networks (2022)
Journal Article
Vadera, S., & Ameen, S. (2022). Methods for pruning deep neural networks. IEEE Access, 63280- 63300. https://doi.org/10.1109/ACCESS.2022.3182659

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
Ameen, S., & Vadera, S. (2020). Pruning neural networks using multi-armed bandits. Computer Journal, 63(7), 1099-1108. https://doi.org/10.1093/comjnl/bxz078

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
Ameen, S., & Vadera, S. (2017). A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images. Expert Systems, 34(3), e12197. https://doi.org/10.1111/exsy.12197

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.