I am a Lecturer (Assistant Professor) in Machine Learning and Computer Vision at the School of Engineering in the University of Edinburgh. I am a member of the Bayesian and Neural Systems research group.
My research interests include:
- simplifying machine learning
- neural architecture search (and AutoML more generally)
- efficient network training
- low-resource deep learning
- engineering applications of machine learning
Previously, I was a postdoc at the School of Informatics in Edinburgh. I have an MEng in Engineering Science and a DPhil (PhD), both from the University of Oxford.
News
- May 2023. (Preannouncement) It is very likely that I will have full funding for a UK student to start a PhD with me this autumn/winter on low-resource neural network training. Please email me if you are interested!
- May 2023. I am co-organising the The First Edinburgh Workshop on Affordable Machine Learning to be held in June.
- January 2023. I am co-organising the Fourth Workshop on Neural Architecture Search, Third lightweight NAS challenge at CVPR 2023.
Latest Work
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
ICLR 2023 (Accepted as a notable paper) 
Chenhongyi Yang*, Jiarui Xu*, Shalini De Mello, Elliot J. Crowley, Xiaolong Wang
A new vision transformer architecture that serves as an excellent backbone across different fine-grained vision tasks.
Plug and Play Active Learning for Object Detection
Preprint, November 2022
Chenhongyi Yang, Lichao Huang, Elliot J. Crowley
An active learning framework that generalises across detectors, requiring no changes to training routines or architectures.
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
ICLR 2023 (Accepted as a notable paper)
Chenhongyi Yang*, Jiarui Xu*, Shalini De Mello, Elliot J. Crowley, Xiaolong Wang
A new vision transformer architecture that serves as an excellent backbone across different fine-grained vision tasks.
Plug and Play Active Learning for Object Detection
Preprint, November 2022
Chenhongyi Yang, Lichao Huang, Elliot J. Crowley
An active learning framework that generalises across detectors, requiring no changes to training routines or architectures.
Selected Publications
Prediction-Guided Distillation for Dense Object Detection
ECCV 2022
Chenhongyi Yang, Mateusz Ochal, Amos Storkey, Elliot J. Crowley
A knowledge distillation framework for single stage detectors that uses a few key predictive regions to obtain high performance.
Neural Architecture Search without Training
ICML 2021 (Long talk)
Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley
A low-cost measure for scoring networks at initialisation that can be used to perform neural architecture search in seconds.
Neural Architecture Search as Program Transformation Exploration
ASPLOS 2021 (Distinguished Paper)
Jack Turner, Elliot J. Crowley, Michael O'Boyle
A compiler-oriented approach to neural architecture search which can generate new convolution operations.
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
NeurIPS 2020 (Spotlight)
Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey
A simple Bayesian alternative to standard meta-learning.
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget
ICLR 2020
Jack Turner*, Elliot J. Crowley*, Michael O'Boyle, Amos Storkey, Gavia Gray
A fast algorithm for obtaining a compressed network architecture using Fisher information.
Prediction-Guided Distillation for Dense Object Detection
ECCV 2022
Chenhongyi Yang, Mateusz Ochal, Amos Storkey, Elliot J. Crowley
A knowledge distillation framework for single stage detectors that uses a few key predictive regions to obtain high performance.
Neural Architecture Search without Training
ICML 2021 (Long talk)
Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley
A low-cost measure for scoring networks at initialisation that can be used to perform neural architecture search in seconds.
Neural Architecture Search as Program Transformation Exploration
ASPLOS 2021 (Distinguished Paper)
Jack Turner, Elliot J. Crowley, Michael O'Boyle
A compiler-oriented approach to neural architecture search which can generate new convolution operations.
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
NeurIPS 2020 (Spotlight)
Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey
A simple Bayesian alternative to standard meta-learning.
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget
ICLR 2020
Jack Turner*, Elliot J. Crowley*, Michael O'Boyle, Amos Storkey, Gavia Gray
A fast algorithm for obtaining a compressed network architecture using Fisher information.
Thanks to Jack Turner and Chenhongyi Yang for the website template.