Publications List

• Dongge Han, Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Peter Bell, Amos Storkey (2024) LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots. International Conference on Computational Linguistics (COLING)

• Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley (2024) einspace: Searching for Neural Architectures from Fundamental Operations. Advances in Neural Information Processing Systems (NeurIPS)

• Eloi Alonso*, Adam Jelley*, Vincent Micheli, Anssi Kanervisto, Amos Storkey, Tim Pearce, François Fleuret (2024) Diffusion for World Modeling: Visual Details Matter in Atari. Advances in Neural Information Processing Systems (NeurIPS)

• Alessandro Fontanella, Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Sarah Parisot (2024) Generating Compositional Scenes via Text-to-image RGBA Instance Generation. Advances in Neural Information Processing Systems (NeurIPS)

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

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

• Chenhongyi Yang, Tianwei Lin, Lichao Huang, Elliot J. Crowley (2024) WidthFormer: Toward Efficient Transformer-based BEV View Transformation. IEEE / RSJ International Conference on Intelligent Robots and Systems (IROS)

• Alessandro Fontanella, Grant Mair, Joanna Wardlaw, Emanuele Trucco, Amos Storkey (2024) Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images. IEEE Transactions on Medical Imaging

• Matt van den Nieuwenhuijzen, Carola Doerr, Henry Gouk, Jan N. van Rijn (2024) Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features. International Conference on Automated Machine Learning (AutoML Workshop Track)

• Henry Gouk, Boyan Gao (2024) Automated Prior Elicitation from Large Language Models for Bayesian Logistic Regression. International Conference on Automated Machine Learning (AutoML Workshop Track)

• Perry Gibson, José Cano, Elliot J. Crowley, Amos Storkey, Michael O'Boyle (2024) DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration. ACM Transactions on Architecture and Code Optimization

• Fady Rezk, Antreas Antoniou, Henry Gouk, Timothy Hospedales (2024) Liouna: Biologically Plausible Learning for Efficient Pre-Training of Transferrable Deep Models. ICML Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization

• 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

• Thomas L. Lee, Amos Storkey (2024) Chunking: Continual Learning is not just about Distribution Shift. Third Conference on Lifelong Learning Agents (CoLLAs 2024)

• Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Amos Storkey (2024) Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning. Reinforcement Learning Conference (RLC)

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

• Henry Gouk, Ondrej Bohdal, Da Li, Timothy Hospedales (2024) On the Limitations of General Purpose Domain Generalisation Methods.

• Thomas L. Lee, Amos Storkey (2024) Approximate Bayesian Class-Conditional Models under Continuous Representation Shift. International Conference on Artificial Intelligence and Statistics (AISTATS 2024)

• Luke Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker, Amos Storkey (2024) DAM: Towards a Foundation Model for Forecasting. International Conference on Learning Representations (ICLR)

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

• 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

• 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.

• 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)

• 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: Fisher-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 GPUs. 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 ResNet 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 DenseNets for Relational Reasoning.

• Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos Storkey (2018) CINIC-10 is not ImageNet 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 Hamiltonian Monte Carlo. 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 Langevin Thermostat for Large-Scale Bayesian 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

• Ksenia A. Kuznetsova, Susana Munoz Maniega, Stuart J. Ritchie, Simon R. Cox, Amos J. Storkey, John M. Starr, Joanna M. Wardlaw, Ian J. Deary, Mark E. Bastin (2015) Brain White Matter Structure and Information Processing Speed in Healthy Older Age. Brain Structure and Function

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

• Colin R. Buchanan and Leslie D. Pettit and Amos Storkey and Sharon Abrahams and Mark E. Bastin (2015) Reduced Structural Connectivity within a Prefrontal-Motor-Subcortical Network in Amyotrophic Lateral Sclerosis. Journal of Magnetic Resonance Imaging

• Amos Storkey, Zhanxing Zhu (2015) Aggregation Under Bias: Renyi Divergence Aggregation and its Implementation via Machine Learning Markets. Proceedings of ECML/PKDD 2015

• Andrew M. Dai, Amos Storkey (2015) The Supervised Hierarchical Dirichlet 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 Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes. IEEE Transactions on Signal Processing

• Colin R Buchanan, Cyril R Pernet, Krzysztof J Gorgolewski, Amos Storkey, Mark E Bastin (2014) Test-Retest Reliability of Structural Brain Networks from Diffusion MRI. NeuroImage

• Peter Orchard, Felix Agakov, Amos Storkey (2013) Bayesian Inference in Sparse Gaussian Graphical Models.

• Tom D. Kitchin, B. Rowe, M. Gill, C. Heymans, R. Massey, D. Witherick, F. Courbin, K. Georgatzis, M. Gentile, D. Gruen, M. Kilbinger, G.L. Li, A.P. Mariglis, G. Meylan, Amos Storkey, B. Xin (2013) Image Analysis for Cosmology: Results from the GREAT10 Star Challenge. Astrophysical Journal Supplement Series

• David P. Reichert, Peggy Series, Amos Storkey (2013) Charles Bonnet Syndrome: Evidence for a Generative Model in the Cortex?. PLOS Computational Biology;

• Krzysztof J. Gorgolewski, Amos Storkey, Mark E. Bastin, Ian R. Whittle, Cyril R. Pernet (2013) Single subject fMRI Test-Retest Reliability Metrics and Confounding Factors. Nueroimage

• Krzysztof J. Gorgolewski, Amos Storkey, Mark E. Bastin, Ian R. Whittle, Joanna M. Wardlaw, Cyril R. Pernet (2013) A Test-Retest Functional MRI Dataset for Motor, Language and Spatial Attention Functions. Gigascience

• Yichuan Zhang, Charles Sutton, Amos Storkey, Zoubin Ghahramani (2012) Continuous relaxations for discrete Hamiltonian Monte-Carlo. Advances in Neural Information Processing Systems (NIPS 2012)

• Simon Lyons, Simo Särkkä, Amos Storkey (2012) The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes. Advances in Neural Information Processing Systems 25 (NIPS2012)

• Krzysztof J. Gorgolewski, Amos Storkey, Mark E. Bastin, Cyril R. Pernet (2012) Adaptive Thresholding for Reliable Topological Inference in Single Subject fMRI Analysis. Frontiers in Human Neuroscience

• 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)

• Felix V. Agakov, Peter Orchard, Amos Storkey (2012) Discriminative Mixtures of Sparse Latent Fields for Stress Testing. International Conference on AI in Statistics (AISTATS)

• David P. Reichert, Peggy Series, Amos Storkey (2011) Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability. Advances in Neural Information Processing Systems 24 (NIPS2011)

• Lawrence Murray, Amos Storkey (2011) Particle Smoothing in Continuous Time: a Fast Approach via Density Estimation. Frontiers in Human Neuroscience

• Jon D. Clayden, Susana Munoz Maniega, Amos Storkey, Martin D. King, Mark E. Bastin, Chris A. Clark (2011) Tractor: Magnetic Resonance Imaging and Tractography with R. Frontiers in Human Neuroscience

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

• Mark Toussaint, Amos Storkey, Stephan Harmeling (2011) Expectation-Maximization Methods for Solving (PO)MDPs and Optimal Control Problems. Silvia Chiappa, David Barber (Eds.) Bayesian Time Series Models, Cambridge University Press.

• Andrew Dai, Amos Storkey (2011) The Grouped Author-Topic Model for Unsupervised Entity Resolution. International Conference on Artificial Neural Networks (ICANN)

• David P. Reichert, Peggy Series, Amos Storkey (2011) A Hierarchical Generative Model of Recurrent Object-Based Attention in the Visual Cortex. International Conference on Artificial Neural Networks (ICANN)

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

• Felix V. Agakov, Paul McKeigue, Jon Krohn, Amos Storkey (2010) Sparse Instrumental Variables (SPIV) for Genome-Wide Studies. Advances in Neural Information Processing Systems 23 (NIPS2010)

• David P. Reichert, Peggy Series, Amos Storkey (2010) Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model. Advances in Neural Information Processing Systems 23 (NIPS2010)

• Amos Storkey (2009) When Training and Test Sets are Different: Characterising Learning Transfer. In Dataset Shift in Machine Learning, Eds Candela, Sugiyama, Schwaighofer, Lawrence. MIT Press.

• Jonathan D. Clayden, Amos Storkey, Susana Munoz Maniega, Mark E. Bastin (2009) Reproducibility of Tract Segmentation between Sessions using an Unsupervised Modelling-based Approach. Neuroimage

• Mark E. Bastin, Jakob P. Piatowski, Amos Storkey, Laura J. Brown, Alistair M. Maclullich, Jonathan D. Clayden (2008) Tract Shape Modelling Provides Evidence of Topological Change in Corpus Callosum Genu During Normal Ageing. Neuroimage

• Ben H. Williams, Marc Toussaint, Amos Storkey (2008) Modelling Motion Primitives and Their Timing in Biologically Executed Movements. Advances in Neural Information Processing Systems 20 (NIPS2007)

• Lawrence Murray, Amos Storkey (2008) Continuous Time Particle Filtering for fMRI. Advances in Neural Information Processing Systems 20 (NIPS2007)

• Jonathan D. Clayden, Mark E. Bastin, Amos Storkey (2007) A Probabilistic Model-based Approach to Consistent White Matter Tract Segmentation. IEEE Transactions on Medical Imaging

• Jonathan D. Clayden, Mark E. Bastin, Amos Storkey (2007) A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation. Proceedings of the ISMRM 15th Scientific Meeting and Exhibition

• Amos Storkey, Masashi Sugiyama (2007) Mixture Regression for Covariate Shift. Advances in Neural Information Processing Systems 19 (NIPS2006)

• Amos Storkey, Enrico Simonotto, Heather Whalley, Stephen Lawrie, Lawrence Murray, David McGonigle (2007) Learning Structural Equation Models for fMRI. Advances in Neural Information Processing Systems 19 (NIPS2006)

• Ben H. Williams, Marc Toussaint, Amos Storkey (2007) A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data". Twentieth International Joint Conference on Artificial Intelligence (IJCAI)

• Marc Toussaint, Stefan Harmeling, Amos Storkey (2006) Probabilistic Inference for Solving (PO)MDPs. Informatics Research Report 0934

• Jonathan D. Clayden, Mark E. Bastin, Amos Storkey (2006) Automated Assessment of Tract Similarity in Group Diffusion MRI Data. Proceedings of the ISMRM 14th Scientific Meeting & Exhibition, Seattle, USA

• Jonathan D. Clayden, Mark E. Bastin, Amos Storkey (2006) Neighbourhood Tractography: a New Approach to Seed Point Placement for Group Fibre Tracking. Proceedings of the Annual Meeting of the ISMRM British Chapter, Guildford, UK

• Ben H. Williams, Marc Toussaint, Amos Storkey (2006) Extracting Motion Primitives from Natural Handwriting Data. International Conference on Artificial Neural Networks (ICANN)

• Jonathan D. Clayden, Mark E. Bastin, Amos Storkey (2006) Improved Segmentation Reproducibility in Group Tractography using a Quantitative Tract Similarity Measure. Neuroimage

• Marc Toussaint, Amos Storkey (2006) Probabilistic inference for solving discrete and continuous state Markov Decision Processes. ICML06 - Proceedings of the 23rd international conference on Machine learning

• Amos Storkey, Nigel C. Hambly, Christopher K.I. Williams, Robert G. Mann (2004) Cleaning Sky Survey Databases using [Hough Transform and Renewal String Approaches. Monthly Notices of the Royal Astronomical Society

• Helen Duncan, Alan Bundy, John Levine, Amos Storkey and Martin Pollet (2004) The use of data mining for the automatic formation of tactics. Proceedings of the Workshop on Computer-Supported Mathematical Theory Development held at the Second International Joint Conference on Automated Reasoning (IJCAR-04),

• Amos Storkey, Michael Allan (2004) Cosine Transform Priors for Enhanced Decoding of Compressed Images. Proceedings of Fifth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2004)

• Amos Storkey (2004) Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data. In Advanced Neural Information Processing Systems 16 (NIPS2003)

• Amos Storkey, Christopher K.I. Williams (2003) Image Modelling with Position-Encoding Dynamic Trees. IEEE Pattern Analysis and Machine Intelligence

• Amos Storkey, Nigel C. Hambly, Christopher K.I. Williams, Robert G. Mann (2003) Renewal Strings for Cleaning Astronomical Databases. In Uncertainty in Artificial Intelligence 19 (UAI-2003)

• Amos Storkey (2003) Dynamic Structure Super-Resolution. Advances in Neural Information Processing Systems 15 (NIPS2002)

• Amos Storkey (2002) Scientific Data Mining, Integration and Visualisation. Technical Report UKeS-2002-06, National E-Science Centre.

• Nicholas J. Adams, Amos Storkey, Christopher K.I. Williams (2001) Comparing Mean Field and Exact EM in Tree-Structured Belief Networks. Proceedings of Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry

• Amos Storkey, Christopher K.I. Williams (2001) Dynamic Positional Trees for Structural Image Analysis. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3 286-292

• Amos Storkey (2000) Dynamic Trees - A Structured Variational Method Giving Efficient Propagation Rules. Conference on Uncertainty in Artificial Intelligence (UAI)

• Nicholas J. Adams, Amos Storkey, Zoubin Ghahramani, Christopher K.I. Williams (2000) MFDT: Mean Field Dynamic Trees. International Conference on Pattern Recognition (ICPR2000)

• Amos Storkey, Romain Valabregue (1999) The Basins of Attraction of a New Hopfield Learning Rule. Neural Networks

• Amos Storkey (1999) Efficient Covariance Matrix Methods for Bayesian Gaussian Processes and Hopfield Neural Networks. Imperial College

• Amos Storkey (1999) Truncated Covariance Matrices and Toeplitz Methods in Gaussian Processes. Ninth International Conference on Artificial Neural Networks - ICANN 99

• Amos Storkey (1998) Palimpsest Memories: a New High Capacity Forgetful Learning Rule. Imperial College Technical Reports

• Amos Storkey (1998) Gaussian Processes for Switching Regimes. Proceedings of the 8th International Conference on Artificial Neural Networks (ICANN98)

• Amos Storkey (1997) Increasing the capacity of the Hopfield network without sacrificing functionality. 7th International Conference an Artificial Neural Networks (ICANN97)

• Amos Storkey, Romain Valabregue (1997) A Hopfield Learning Rule with High Capacity Storage of Time-Correlated Patterns. Electronic Letters

• Christopher Browne, Joel de L. Pereira Castro Jr, Amos Storkey (1996) A Modified Spreading Algorithm for Autoassociation in Weightless Neural Networks. 6th International Conference an Artificial Neural Networks (ICANN96)

• Amos Storkey (1996) The Fractal and Multifractal Nature of Traffic. Universities Transport Studies Group

• Amos Storkey (1995) Fractals and Chaos in Traffic Flows. Imperial college London