Publications List

conference workshop preprint journal tech report

• Ke Wang, Ningyuan Shan, Henry Gouk, Iris Szu-Szu Ho (2024) Skin Malignancy Classification Using Patients' Skin Images and Meta-data: Multimodal Fusion for Improving Fairness. Medical Imaging with Deep Learning

• Chenhongyi Yang, Lichao Huang, Elliot J. Crowley (2024) Plug and Play Active Learning for Object Detection. To appear at the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)

• Thomas L. Lee, Amos Storkey (2024) Class Conditional Gaussians for Continual Learning. To appear at the International Conference on Artificial Intelligence and Statistics (AISTATS)

• Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson, Elliot J. Crowley, Amos Storkey (2024) Hyperparameter Selection in Continual Learning.

• Chenhongyi Yang, Anastasia Tkach, Shreyas Hampali, Linguang Zhang, Elliot J. Crowley, Cem Keskin (2024) EgoPoseFormer: A Simple Baseline for Egocentric 3D Human Pose Estimation.

• Chenhongyi Yang, Zehui Chen, Miguel Espinosa, Linus Ericsson, Zhenyu Wang, Jiaming Liu, Elliot J. Crowley (2024) PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognition.

• Chenhongyi Yang, Tianwei Lin, Lichao Huang, Elliot J. Crowley (2024) WidthFormer: Toward Efficient Transformer-based BEV View Transformation.

• Fady Rezk, Antreas Antoniou, Henry Gouk, Timothy Hospedales (2023) Is Scaling Learned Optimizers Worth It? Evaluating The Value of VeLO's 4000 TPU Months. I Can't Believe It's Not Better! (NeurIPS Workshop)

• Miguel Espinosa, Elliot J. Crowley (2023) Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps. NeurIPS 2023 Workshop on Diffusion Models

• Perry Gibson, José Cano, Elliot J. Crowley, Amos Storkey, Michael O'Boyle (2023) DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack.

• Ruchika Chavhan, Henry Gouk, Da Li, Timothy Hospedales (2023) Quality Diversity for Visual Pre-Training. International Conference on Computer Vision

• Alan D Fleming, Joseph Mellor, Stuart J McGurnaghan, Luke A K Blackbourn, Keith A Goatman, Caroline Styles, Amos J Storkey, Paul M McKeigue, Helen M Colhoun (2023) Deep learning detection of diabetic retinopathy in Scotland’s diabetic eye screening programme.

• Alessandro Fontanella, Grant Mair, Joanna Wardlaw, Emanuele Trucco, Amos Storkey (2023) Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images.

• Luisa Shimabucoro, Timothy Hospedales, Henry Gouk (2023) Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?. ICML Workshop on Data-Centric Machine Learning Research

• Justin Engelmann, Amos Storkey, Miguel O. Bernabeu (2023) QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models.

• Jamie Burke, Justin Engelmann, Charlene Hamid, Megan Reid-Schachter, Tom Pearson, Dan Pugh, Neeraj Dhaun, Stuart King, Tom MacGillivray, Miguel O Bernabeu, Amos Storkey, Ian JC MacCormick (2023) Efficient and fully-automatic retinal choroid segmentation in OCT through DL-based distillation of a hand-crafted pipeline.

• Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna Wardlaw, Grant Mair, Emanuele Trucco, Amos Storkey (2023) ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging. International Conference on Machine Learning (ICML)

• Ondrej Bohdal, Yinbing Tian, Yongshuo Zong, Ruchika Chavhan, Da Li, Henry Gouk, Li Guo, Timothy Hospedales (2023) Meta omnium: A benchmark for general-purpose learning-to-learn. Computer Vision and Pattern Recognition

• Ruchika Chavhan, Henry Gouk, Jan Stuehmer, Calum Heggan, Mehrdad Taghoobi, Timothy Hospedales (2023) Amortised Invariance Learning for Contrastive Self-Supervision. International Conference on Learning Representations

• Vithya Yogarajan, Gillian Dobbie, Henry Gouk (2023) Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis. International Conference on Learning Representations (Tiny Papers Track)

• Ana Villaplana-Velasco, Marie Pigeyre, Justin Engelmann, Konrad Rawlik, Oriol Canela-Xandri, Claire Tochel, Frida Lona-Durazo, Muthu Rama Krishnan Mookiah, Alex Doney, Esteban J Parra, Emanuele Trucco, Tom MacGillivray, Kristiina Rannikmae, Albert Tenesa, Erola Pairo-Castineira, Miguel O Bernabeu (2023) Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes. Communications Biology

• Adam Jelley, Amos Storkey, Antreas Antoniou, Sam Devlin (2023) Contrastive Meta-Learning for Partially Observable Few-Shot Learning. International Conference on Learning Representations (ICLR)

• Chenhongyi Yang, Jiarui Xu, Shalini De Mello, Elliot J. Crowley, Xiaolong Wang (2023) GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation. International Conference on Learning Representations (ICLR)

• Joseph Mellor, Wenhua Jiang, Alan Fleming, Stuart McGurnaghan, Luke Blackbourn, Caroline Styles, Amos J Storkey, Paul M McKeigue, Helen M Colhoun, Scottish Diabetes Research Network Epidemiology Group (2023) Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland. International Journal of Medical Informatics

• Asif Khan, Amos Storkey (2023) Adversarial robustness of β−VAE through the lens of local geometry. International Conference on Artificial Intelligence and Statistics (AISTATS)

• Justin Engelmann, Alice D. McTrusty, Ian J. C. MacCormick, Emma Pead, Amos Storkey, Miguel O. Bernabeu (2022) Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions. Nature Machine Intelligence

• Asif Khan, Amos Storkey (2022) Hamiltonian Latent Operators for content and motion disentanglement in image sequences. Advances in Neural Information Processing Systems

• Chenhongyi Yang, Mateusz Ochal, Amos Storkey, Elliot J. Crowley (2022) Prediction-Guided Distillation for Dense Object Detection. European Conference on Computer Vision

• Bryan M Li, Leonardo V Castorina, Maria Del Carmen Valdés Hernández, Una Clancy, Stewart J Wiseman, Eleni Sakka, Amos J Storkey, Daniela Jaime Garcia, Yajun Cheng, Fergus Doubal, Michael T Thrippleton, Michael Stringer, Joanna M Wardlaw (2022) Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols. Front Comput Neurosci

• Justin Engelmann, Ana Villaplana-Velasco, Amos Storkey, Miguel O. Bernabeu (2022) Robust and efficient computation of retinal fractal dimension through deep approximation. 9th MICCAI Workshop on Ophthalmic Medical Image Analysis at MICCAI 2022

• Lukas Schäfer, Filippos Christianos, Amos Storkey, Stefano V. Albrecht (2022) Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning.

• Justin Engelmann, Amos Storkey, Miguel O. Bernabeu (2021) Global explainability in aligned image modalities. Interpretable Machine Learning in Healthcare at ICML 2022

• Paul Micaelli, Amos Storkey (2021) Gradient-based Hyperparameter Optimization Over Long Horizons. Advances in Neural Information Processing Systems

• Chenhongyi Yang, Lichao Huang, Elliot J. Crowley (2021) Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning.

• Benedict Leimkuhler, Tiffany Vlaar, Timothée Pouchon, Amos Storkey (2021) Better Training using Weight-Constrained Stochastic Dynamics. International Conference on Machine Learning (ICML)

• Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley (2021) Neural Architecture Search without Training. International Conference on Machine Learning (ICML)

• Elliot J. Crowley, Gavia Gray, Jack Turner, Amos Storkey (2021) Substituting Convolutions for Neural Network Compression. IEEE Access

• Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey, Jose Vazquez, Sen Wang (2021) How Sensitive are Meta-Learners to Dataset Imbalance?. ICLR Learning to Learn Workshop

• Timothy Hospedales, Antreas Antoniou, Paul Micaelli, Amos Storkey (2021) Meta-Learning in Neural Networks: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence

• Jack Turner, Elliot J. Crowley, Michael O'Boyle (2021) Neural Architecture Search as Program Transformation Exploration. International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

• Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey, Jose Vazquez, Sen Wang (2021) Few-Shot Learning with Class Imbalance.

• Massimiliano Patacchiola, Amos Storkey (2020) Self-Supervised Relational Reasoning for Representation Learning. Advances in Neural Information Processing Systems (NeurIPS)

• Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey (2020) Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels. Advances in Neural Information Processing Systems (NeurIPS)

• Benedict Leimkuhler, Timothée Pouchon, Tiffany Vlaar, Amos Storkey (2020) Constraint-Based Regularisation of Neural Networks. NeurIPS OPT2020: 12th Annual Workshop on Optimization for Machine Learning

• Alessandro Fontanella, Emma Pead, Tom MacGillivray, Miguel O. Bernabeu, Amos Storkey (2020) Classification with a domain shift in medical imaging. Med-NeurIPS 2020: Medical Imaging meets NeurIPS Workshop

• Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, Amos Storkey (2020) Defining Benchmarks for Continual Few-Shot Learning. NeurIPS MetaLearn 2020: Workshop on Meta-Learning

• Luke N. Darlow, Stanisław Jastrzębski, Amos Storkey (2020) Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks.

• Perry Gibson, José Cano, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey (2020) Optimizing Grouped Convolutions on Edge Devices. International Conference on Application-specific Systems, Architectures and Processors (ASAP)

• Stathi Fotiadis, Eduardo Pignatelli, Mario Lino Valencia, Chris Cantwell, Amos Storkey, Anil A. Bharath (2020) Comparing Recurrent and Convolutional Neural Networks for Predicting Wave Propagation. Workshop on Deep Learning and Differential Equations, ICLR

• Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos Storkey, Gavia Gray (2020) {BlockSwap}: {F}isher-guided Block Substitution for Network Compression on a Budget. International Conference on Learning Representations (ICLR)

• Luke N. Darlow, Amos Storkey (2020) {DHOG}: Deep Hierarchical Object Grouping.

• Antreas Antoniou, Amos Storkey (2019) Learning to Learn via Self-Critique. Advances in Neural Information Processing Systems (NeurIPS)

• Paul Micaelli, Amos Storkey (2019) Zero-shot Knowledge Transfer via Adversarial Belief Matching. Advances in Neural Information Processing Systems (NeurIPS)

• Valentin Radu, Kuba Kaszyk, Yuan Wen, Jack Turner, José Cano, Elliot J. Crowley, Björn Franke, Amos Storkey, Michael O’Boyle (2019) Performance Aware Convolutional Neural Network Channel Pruning for Embedded {GPU}s. International Symposium on Workload Characterization (IISWC)

• Gavia Gray, Elliot J. Crowley, Amos Storkey (2019) Separable Layers Enable Structured Efficient Linear Substitutions.

• Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov (2019) Exploration by Random Network Distillation. International Conference on Learning Representations (ICLR)

• Antreas Antoniou, Harrison Edwards, Amos Storkey (2019) How to train your {MAML}. International Conference on Learning Representations (ICLR)

• Yuri Burda, Harrison Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, Alexei A. Efros (2019) Large-Scale Study of Curiosity-Driven Learning. International Conference on Learning Representations (ICLR)

• Stanisław Jastrzębski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey (2019) On the Relation Between the Sharpest Directions of {DNN} Loss and the {SGD} Step Length. International Conference on Learning Representations (ICLR)

• Jack Turner, Elliot J. Crowley, Valentin Radu, José Cano, Amos Storkey, Michael O'Boyle (2019) Distilling with Performance Enhanced Students.

• Antreas Antoniou, Amos Storkey (2019) Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation.

• Luke N. Darlow, Amos Storkey (2019) What Information Does a {R}es{N}et Compress?.

• Elliot J. Crowley, Jack Turner, Amos Storkey, Michael O'Boyle (2018) Pruning Neural Networks: Is it Time to Nip It in the Bud?. Workshop on Compact Deep Neural Networks with industrial applications, NeurIPS

• Elliot J. Crowley, Gavia Gray, Amos Storkey (2018) Moonshine: Distilling with Cheap Convolutions. Advances in Neural Information Processing Systems (NeurIPS)

• Antreas Antoniou, Agnieszka Słowik, Elliot J. Crowley, Amos Storkey (2018) Dilated {D}ense{N}ets for Relational Reasoning.

• Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos Storkey (2018) {CINIC-10} is not {I}mage{N}et or {CIFAR-10}.

• Luke N. Darlow, Amos Storkey (2018) {GINN}: Geometric Illustration of Neural Networks.

• Antreas Antoniou, Amos Storkey, Harrison Edwards (2018) Augmenting Image Classifiers using Data Augmentation Generative Adversarial Networks. International Conference on Artificial Neural Networks (ICANN)

• Stanisław Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos Storkey (2018) Three Factors Influencing Minima in {SGD}. International Conference on Artificial Neural Networks (ICANN)

• Jack Turner, José Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey (2018) Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks. International Symposium on Workload Characterization (IISWC)

• Matt Graham, Amos Storkey (2017) Asymptotically Exact Inference in Differentiable Generative Models. Electronic Journal of Statistics

• Antreas Antoniou, Amos Storkey, Harrison Edwards (2017) Data Augmentation Generative Adversarial Networks.

• Matt Graham, Amos Storkey (2017) Continuously Tempered {H}amiltonian {M}onte {C}arlo. Conference on Uncertainty in Artificial Intelligence (UAI)

• Matt Graham, Amos Storkey (2017) Asymptotically Exact Inference in Differentiable Generative Models. International Conference on Artificial Intelligence and Statistics (AISTATS)

• Harrison Edwards, Amos Storkey (2017) Towards a Neural Statistician. International Conference on Learning Representations (ICLR)

• Gavia Gray, Amos Storkey (2016) Resource-Efficient Feature Gathering at Test Time. Workshop on Reliable Machine Learning in the Wild, NeurIPS

• Harrison Edwards, Amos Storkey (2016) Censoring Representations with an Adversary. International Conference on Learning Representations (ICLR)

• Cyril Pernet, Krzysztof J Gorgolewski, Dominic Job, David Rodriguez, Amos J Storkey, Ian Whittle, Joanna Wardlaw (2016) Evaluation of a Pre-surgical Functional {MRI} Workflow: From Data Acquisition to Reporting. International Journal of Medical Informatics

• Zhanxing Zhu, Amos Storkey (2016) Stochastic Parallel Block Coordinate Descent for Large-scale Saddle Point Problems. AAAI Conference on Artificial Intelligence (AAAI)

• Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos Storkey (2015) Covariance-Controlled Adaptive {L}angevin Thermostat for Large-Scale {B}ayesian Sampling. Advances in Neural Information Processing Systems (NeurIPS)

• Zhanxing Zhu, Amos Storkey (2015) Adaptive Stochastic Primal-dual Coordinate Descent for Separable Saddle Point Problems. Joint European Conference on Machine Learning and Knowledge Discovery in Databases

• Chris Clark, Amos Storkey (2015) Training Deep Convolutional Neural Networks to Play {G}o. International Conference on Machine Learning (ICML)

• Andrew M. Dai, Amos Storkey (2015) The Supervised Hierarchical {D}irichlet process. IEEE Transactions on Pattern Analysis and Machine Intelligence (Special Issue on Bayesian Nonparametrics)

• Jinli Hu, Amos Storkey (2014) Multi-period Trading Prediction Markets with Connections to Machine Learning. International Conference on Machine Learning (ICML)

• Simon Lyons, Simo Särkkä, Amos Storkey (2014) Series Expansion Approximations of {B}rownian Motion for Non-Linear {K}alman Filtering of Diffusion Processes. IEEE Transactions on Signal Processing

• Peter Orchard, Felix Agakov, Amos Storkey (2013) {B}ayesian Inference in Sparse {G}aussian Graphical Models.

• Athina Spiliopoulou, Amos Storkey (2012) A Topic Model for Melodic Sequences. International Conference on Machine Learning (ICML)

• Amos Storkey, Jono Millin, Krzysztof Geras (2012) Isoelastic Agents and Wealth Updates in Machine Learning Markets. International Conference on Machine Learning (ICML)

• Athina Spiliopoulou, Amos Storkey (2011) Comparing Probabilistic Models for Melodic Sequences. Proceedings of the ECML-PKDD

• Amos Storkey (2011) Machine Learning Markets. International Conference on Artificial Intelligence and Statistics (AISTATS)