I am a Lecturer (Assistant Professor) in Machine Learning and Computer Vision at the School of Engineering, University of Edinburgh. I co-lead the Bayesian and Neural Systems research group.
My research interests include:
- simplifying machine learning
- AutoML, especially neural architecture search
- efficient network training
- low-resource deep learning
- engineering applications of machine learning
I have an MEng in Engineering Science and a DPhil (PhD), both from the University of Oxford. My DPhil was on "Visual recognition in Art using Machine Learning" with Andrew Zisserman in the VGG group. After my DPhil, I was a postdoc at the School of Informatics in Edinburgh with Amos Storkey.
I hold an EPSRC New Investigator Award and I am an investigator on the dAIEdge Horizon Network.Team
I am principal advisor for:- Dr Linus Ericsson (Postdoc) who is exploring the fundamentals of neural architectures
- Chenhongyi Yang (PhD student) who works on 2D and 3D visual recognition
- Miguel Espinosa (PhD student) who works on engineering big models for earth observation
- Shiwen Qin (PhD student) who works on efficient training of LLMs
News
- July 2024. Our PlainMamba paper was accepted to BMVC 2024.
- July 2024. Amos Storkey and I are recruiting a postdoc (2 years) in Continual Machine Learning at the Edge. Please contact Amos with any informal enquiries at amos+daiedge *at* inf.ed.ac.uk.
- July 2024. Our pose estimation paper was accepted to ECCV 2024, and our transformer-based Bird's-Eye-View 3D detection paper was accepted to IROS 2024
- June 2024. We have created einspace: an expressive search space for NAS. For a high level intro and an interactive architecture visualiser, see the project page.
- April 2024. More preprints! A strong, simple baseline for egocentric 3D pose estimation and an investigation into HPO for continual learning.
- March 2024. We have joined the Mamba bandwagon (Mambwagon?) in our latest preprint!
- February 2024. Chenhongyi's paper on active learning for object detection was accepted to CVPR 2024
- February 2024. I will be a co-investigator on the EPSRC AI Hub for Causality in Healthcare AI with Real Data (CHAI)
- January 2024. I have been nominated for an EUSA Teaching Award
- January 2024. I am co-organising this year's NAS Unseen-Data competition as part of AutoML 2024
- January 2024. I am co-organising the Fifth Workshop on Neural Architecture Search at CVPR 2024
Selected Publications
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation
ICLR 2023 (Accepted as a notable paper) ![](resources/github.svg)
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.
Prediction-Guided Distillation for Dense Object Detection
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
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) ![](resources/github.svg)
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
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
Jack Turner*, Elliot J. Crowley*, Michael O'Boyle, Amos Storkey, Gavia Gray
A fast algorithm for obtaining a compressed network architecture using Fisher information.
![](resources/gpvit.png)
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.
![](resources/pgd.png)
Prediction-Guided Distillation for Dense Object Detection
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.
![](resources/naswot.png)
Neural Architecture Search without Training
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.
![](resources/poly.png)
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.
![](resources/gp.png)
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey
A simple Bayesian alternative to standard meta-learning.
![](resources/blockswap.png)
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget
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.