3 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Separable Layers Enable Structured Efficient Linear Substitutions
Gavin Gray, Elliot J. Crowley, Amos Storkey
In response to the development of recent efficient dense layers, this paper shows that something as simple as replacing linear components in pointwise convolutions with structured…
Dilated DenseNets for Relational Reasoning
Antreas Antoniou, Agnieszka Słowik, Elliot J. Crowley +1
Despite their impressive performance in many tasks, deep neural networks often struggle at relational reasoning. This has recently been remedied with the introduction of a plug-in…
Distilling with Performance Enhanced Students
Jack Turner, Elliot J. Crowley, Valentin Radu +3
The task of accelerating large neural networks on general purpose hardware has, in recent years, prompted the use of channel pruning to reduce network size. However, the efficacy o…
A Closer Look at Structured Pruning for Neural Network Compression
Elliot J. Crowley, Jack Turner, Amos Storkey +1
Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducin…
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
Jack Turner, José Cano, Valentin Radu +3
Convolutional Neural Networks (CNNs) are extremely computationally demanding, presenting a large barrier to their deployment on resource-constrained devices. Since such systems are…