Jack Turner
cv · github · scholar

I'm a software engineer at Qualcomm R&D, where I work on compilers and machine learning.

Before joining Qualcomm, I did my PhD at the University of Edinburgh, supervised by Professor Michael O’Boyle and Lecturer (Asst. Prof.) Elliot J. Crowley.

My research interests include (but are not limited to!):

  • Architecture rewriting in neural networks
  • Optimising compilers
  • Program transformations

In my spare time I enjoy baking bread, climbing rocks, and using Oxford commas.

Conference papers

18th July, 2021 ICML

Neural Architecture Search Without Training

Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley.

A low-cost measure for scoring networks at initialisation which allows us to perform architecture search in a matter of seconds instead of hours or days.

19th Apr, 2021 ASPLOS

Neural Architecture Search as Program Transformation Exploration

Jack Turner, Elliot J. Crowley, Michael O'Boyle.

A compiler-oriented approach to neural architecture search which allows us to generate new types of convolution.

6th Dec, 2020 NeurIPS

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey.

A Bayesian treatment for the meta-learning inner loop through the use of Gaussian Processes and neural networks.

26th Apr, 2020 ICLR

BlockSwap: Fisher-guided Block Substitution for Network Compression

Jack Turner, Elliot J. Crowley, Amos Storkey, Michael O'Boyle, Gavin Gray.

One-shot compression of neural networks by swapping out residual blocks for cheaper alternatives, guided by Fisher Information at initialisation.

30th Feb, 2018 IISWC

Characterising Across-Stack Optimisations for Deep Neural Networks

Jack Turner, José Cano, Valentin Radu, Elliot J. Crowley, Amos Storkey, Michael O'Boyle.

A study on the interaction of optimisations applied at different levels of the compilation stack for neural networks - from neural architectural decisions through to hardware design.


7th June, 2019 NeurIPS CDNNRIA

Pruning neural networks: is it time to nip it in the bud?

Elliot J. Crowley, Jack Turner, Michael O'Boyle, Amos Storkey.

An investigation into the efficacy of structured pruning for compressing neural networks. We show that simple downscaling schemes can be used to produce more performant networks than their pruned equivalents.