794 citations · 2.7k across the 93 of their papers we have counts for
5 papers · 2 filters
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…
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas +3
Stochastic Gradient Descent (SGD) based training of neural networks with a large learning rate or a small batch-size typically ends in well-generalizing, flat regions of the weight…