62 citations · 150 across the 8 of their papers we have counts for
9 papers
Neural Architecture Search as Program Transformation Exploration
Jack Turner, Elliot J. Crowley, Michael O'Boyle
Improving the performance of deep neural networks (DNNs) is important to both the compiler and neural architecture search (NAS) communities. Compilers apply program transformations…
Optimizing Grouped Convolutions on Edge Devices
Perry Gibson, José Cano, Jack Turner +3
When deploying a deep neural network on constrained hardware, it is possible to replace the network's standard convolutions with grouped convolutions. This allows for substantial m…
Neural Architecture Search without Training
Joseph Mellor, Jack Turner, Amos Storkey +1
The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this de…
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs
Valentin Radu, Kuba Kaszyk, Yuan Wen +6
Convolutional Neural Networks (CNN) are becoming a common presence in many applications and services, due to their superior recognition accuracy. They are increasingly being used o…
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley +2
Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a s…
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget
Jack Turner, Elliot J. Crowley, Michael O'Boyle +2
The desire to map neural networks to varying-capacity devices has led to the development of a wealth of compression techniques, many of which involve replacing standard convolution…