A Modified Spreading Algorithm for Autoassociation in Weightless Neural Networks. - Christopher John Browne; Joel de L. Pereira Castro Jr.; Amos J. Storkey (ICANN, 1996)

Increasing the Capacity of a Hopfield Network without Sacrificing Functionality. - Amos J. Storkey (ICANN, 1997)

The basins of attraction of a new Hopfield learning rule. - Amos J. Storkey; Romain Valabrègue (Neural Networks, 1999)

MFDTs: Mean Field Dynamic Trees. - Nicholas J. Adams; Amos J. Storkey; Christopher K. I. Williams; Zoubin Ghahramani (ICPR, 2000)

Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules. - Amos J. Storkey (UAI, 2000)

Dynamic Positional Trees for Structural Image Analysis. - Amos J. Storkey; Christopher K. I. Williams (AISTATS, 2001)

Dynamic Structure Super-Resolution. - Amos J. Storkey (NIPS, 2002)

Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data. - Amos J. Storkey (NIPS, 2003)

Renewal Strings for Cleaning Astronomical Databases. - Amos J. Storkey; Nigel C. Hambly; Christopher K. I. Williams; Robert G. Mann (UAI, 2003)

Image Modeling with Position-Encoding Dynamic Trees. - Amos J. Storkey; Christopher K. I. Williams (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Cosine Transform Priors for Enhanced Decoding of Compressed Images. - Amos J. Storkey; Michael Allan (IDEAL, 2004)

The 2005 PASCAL Visual Object Classes Challenge. - Mark Everingham; Andrew Zisserman; Christopher K. I. Williams; Luc Van Gool; Moray Allan; Christopher M. Bishop; Olivier Chapelle; Navneet Dalal; Thomas Deselaers; Gyuri Dorkó; Stefan Duffner; Jan Eichhorn; et al. (MLCW, 2005)

Extracting Motion Primitives from Natural Handwriting Data. - Ben H. Williams; Marc Toussaint; Amos J. Storkey (ICANN (2), 2006)

Probabilistic inference for solving discrete and continuous state Markov Decision Processes. - Marc Toussaint; Amos J. Storkey (ICML, 2006)

Mixture Regression for Covariate Shift. - Amos J. Storkey; Masashi Sugiyama (NIPS, 2006)

Learning Structural Equation Models for fMRI. - Amos J. Storkey; Enrico Simonotto; Heather Whalley; Stephen M. Lawrie; Lawrence Murray; David J. McGonigle (NIPS, 2006)

Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure. - Jonathan D. Clayden; Mark E. Bastin; Amos J. Storkey (NeuroImage, 2006)

A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data. - Ben H. Williams; Marc Toussaint; Amos J. Storkey (IJCAI, 2007)

Continuous Time Particle Filtering for fMRI. - Lawrence Murray; Amos J. Storkey (NIPS, 2007)

Modelling motion primitives and their timing in biologically executed movements. - Ben H. Williams; Marc Toussaint; Amos J. Storkey (NIPS, 2007)

A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation. - Jonathan D. Clayden; Amos J. Storkey; Mark E. Bastin (IEEE Trans. Medical Imaging, 2007)

Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing. - Mark E. Bastin; Jakub P. Piatkowski; Amos J. Storkey; Laura J. E. Brown; Alasdair M. J. MacLullich; Jonathan D. Clayden (NeuroImage, 2008)

Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach. - Jonathan D. Clayden; Amos J. Storkey; Susana Muñoz Maniega; Mark E. Bastin (NeuroImage, 2009)

Sparse Instrumental Variables (SPIV) for Genome-Wide Studies. - Felix V. Agakov; Paul McKeigue; Jon Krohn; Amos J. Storkey (NIPS, 2010)

Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model. - David P. Reichert; Peggy Seriès; Amos J. Storkey (NIPS, 2010)

The Grouped Author-Topic Model for Unsupervised Entity Resolution. - Andrew M. Dai; Amos J. Storkey (ICANN (1), 2011)

A Hierarchical Generative Model of Recurrent Object-Based Attention in the Visual Cortex. - David P. Reichert; Peggy Seriès; Amos J. Storkey (ICANN (1), 2011)

Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability. - David P. Reichert; Peggy Seriès; Amos J. Storkey (NIPS, 2011)

Comparing Probabilistic Models for Melodic Sequences. - Athina Spiliopoulou; Amos J. Storkey (ECML/PKDD (3), 2011)

Machine Learning Markets. - Amos J. Storkey (AISTATS, 2011)

Particle Smoothing in Continuous Time: A Fast Approach via Density Estimation. - Lawrence Murray; Amos J. Storkey (IEEE Trans. Signal Process., 2011)

A Topic Model for Melodic Sequences. - Athina Spiliopoulou; Amos J. Storkey (ICML, 2012)

Isoelastic Agents and Wealth Updates in Machine Learning Markets. - Amos J. Storkey; Jono Millin; Krzysztof J. Geras (ICML, 2012)

The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes. - Simon M. J. Lyons; Amos J. Storkey; Simo Särkkä (NIPS, 2012)

Continuous Relaxations for Discrete Hamiltonian Monte Carlo. - Yichuan Zhang; Charles Sutton; Amos J. Storkey; Zoubin Ghahramani (NIPS, 2012)

Discriminative Mixtures of Sparse Latent Fields for Risk Management. - Felix V. Agakov; Peter Orchard; Amos J. Storkey (AISTATS, 2012)

Bayesian Inference in Sparse Gaussian Graphical Models. - Peter Orchard; Felix V. Agakov; Amos J. Storkey (CoRR, 2013)

Single subject fMRI test-retest reliability metrics and confounding factors. - Krzysztof J. Gorgolewski; Amos J. Storkey; Mark E. Bastin; Ian Whittle; Cyril R. Pernet (NeuroImage, 2013)

Charles Bonnet Syndrome: Evidence for a Generative Model in the Cortex? - David P. Reichert; Peggy Seriès; Amos J. Storkey (PLoS Comput. Biol., 2013)

Multi-period Trading Prediction Markets with Connections to Machine Learning. - Jinli Hu; Amos J. Storkey (ICML, 2014)

Teaching Deep Convolutional Neural Networks to Play Go. - Christopher Clark; Amos J. Storkey (CoRR, 2014)

Test-retest reliability of structural brain networks from diffusion MRI. - Colin R. Buchanan; Cyril R. Pernet; Krzysztof J. Gorgolewski; Amos J. Storkey; Mark E. Bastin (NeuroImage, 2014)

Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes. - Simon M. J. Lyons; Simo Särkkä; Amos J. Storkey (IEEE Trans. Signal Process., 2014)

Training Deep Convolutional Neural Networks to Play Go. - Christopher Clark; Amos J. Storkey (ICML, 2015)

Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. - Xiaocheng Shang; Zhanxing Zhu; Benedict J. Leimkuhler; Amos J. Storkey (NIPS, 2015)

Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets. - Amos J. Storkey; Zhanxing Zhu; Jinli Hu (ECML/PKDD (1), 2015)

Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. - Zhanxing Zhu; Amos J. Storkey (ECML/PKDD (1), 2015)

The Supervised Hierarchical Dirichlet Process. - Andrew M. Dai; Amos J. Storkey (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems. - Zhanxing Zhu; Amos J. Storkey (AAAI, 2016)

Censoring Representations with an Adversary. - Harrison Edwards; Amos J. Storkey (ICLR (Poster), 2016)

Evaluation of a pre-surgical functional MRI workflow: From data acquisition to reporting. - Cyril R. Pernet; Krzysztof J. Gorgolewski; Dominic Job; David Rodriguez; Amos J. Storkey; Ian Whittle; Joanna M. Wardlaw (Int. J. Medical Informatics, 2016)

Asymptotically exact inference in differentiable generative models. - Matthew M. Graham; Amos J. Storkey (AISTATS, 2017)

BONSEYES: Platform for Open Development of Systems of Artificial Intelligence: Invited paper. - Tim Llewellynn; Maria del Milagro Fernández-Carrobles; Oscar Déniz; Samuel Fricker; Amos J. Storkey; Nuria Pazos; Gordana Velikic; Kirsten Leufgen; Rozenn Dahyot; Sebastian Koller; Georgios I. Goumas; Peter Leitner; et al. (Conf. Computing Frontiers, 2017)

Towards a Neural Statistician. - Harrison Edwards; Amos J. Storkey (ICLR (Poster), 2017)

Continuously Tempered Hamiltonian Monte Carlo. - Matthew M. Graham; Amos J. Storkey (UAI, 2017)

Data Augmentation Generative Adversarial Networks. - Antreas Antoniou; Amos J. Storkey; Harrison Edwards (CoRR, 2017)

Three Factors Influencing Minima in SGD. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (CoRR, 2017)

Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks. - Antreas Antoniou; Amos J. Storkey; Harrison Edwards (ICANN (3), 2018)

Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICANN (3), 2018)

Finding Flatter Minima with SGD. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICLR (Workshop), 2018)

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks. - Jack Turner; José Cano 001; Valentin Radu; Elliot J. Crowley; Michael F. P. O'Boyle; Amos J. Storkey (IISWC, 2018)

Moonshine: Distilling with Cheap Convolutions. - Elliot J. Crowley; Gavin Gray; Amos J. Storkey (NeurIPS, 2018)

DNN's Sharpest Directions Along the SGD Trajectory. - Stanislaw Jastrzebski; Zachary Kenton; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (CoRR, 2018)

GINN: Geometric Illustration of Neural Networks. - Luke Nicholas Darlow; Amos J. Storkey (CoRR, 2018)

CINIC-10 is not ImageNet or CIFAR-10. - Luke Nicholas Darlow; Elliot J. Crowley; Antreas Antoniou; Amos J. Storkey (CoRR, 2018)

Pruning neural networks: is it time to nip it in the bud? - Elliot J. Crowley; Jack Turner; Amos J. Storkey; Michael F. P. O'Boyle (CoRR, 2018)

HAKD: Hardware Aware Knowledge Distillation. - Jack Turner; Elliot J. Crowley; Valentin Radu; José Cano 001; Amos J. Storkey; Michael F. P. O'Boyle (CoRR, 2018)

Dilated DenseNets for Relational Reasoning. - Antreas Antoniou; Agnieszka Slowik; Elliot J. Crowley; Amos J. Storkey (CoRR, 2018)

How to train your MAML. - Antreas Antoniou; Harrison Edwards; Amos J. Storkey (ICLR (Poster), 2019)

Large-Scale Study of Curiosity-Driven Learning. - Yuri Burda; Harri Edwards; Deepak Pathak; Amos J. Storkey; Trevor Darrell; Alexei A. Efros (ICLR (Poster), 2019)

Exploration by random network distillation. - Yuri Burda; Harrison Edwards; Amos J. Storkey; Oleg Klimov (ICLR (Poster), 2019)

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length. - Stanislaw Jastrzebski; Zachary Kenton; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICLR (Poster), 2019)

Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs. - Valentin Radu; Kuba Kaszyk; Yuan Wen; Jack Turner; José Cano 001; Elliot J. Crowley; Björn Franke; Amos J. Storkey; Michael F. P. O'Boyle (IISWC, 2019)

Learning to Learn By Self-Critique. - Antreas Antoniou; Amos J. Storkey (NeurIPS, 2019)

Zero-shot Knowledge Transfer via Adversarial Belief Matching. - Paul Micaelli; Amos J. Storkey (NeurIPS, 2019)

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

Learning to learn via Self-Critique. - Antreas Antoniou; Amos J. Storkey (CoRR, 2019)

Separable Layers Enable Structured Efficient Linear Substitutions. - Gavin Gray; Elliot J. Crowley; Amos J. Storkey (CoRR, 2019)

BlockSwap: Fisher-guided Block Substitution for Network Compression. - Jack Turner; Elliot J. Crowley; Gavin Gray; Amos J. Storkey; Michael F. P. O'Boyle (CoRR, 2019)

Deep Kernel Transfer in Gaussian Processes for Few-shot Learning. - Massimiliano Patacchiola; Jack Turner; Elliot J. Crowley; Amos J. Storkey (CoRR, 2019)

Optimizing Grouped Convolutions on Edge Devices. - Perry Gibson; José Cano 001; Jack Turner; Elliot J. Crowley; Michael F. P. O'Boyle; Amos J. Storkey (ASAP, 2020)

BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget. - Jack Turner; Elliot J. Crowley; Michael F. P. O'Boyle; Amos J. Storkey; Gavin Gray (ICLR, 2020)

Self-Supervised Relational Reasoning for Representation Learning. - Massimiliano Patacchiola; Amos J. Storkey (NeurIPS, 2020)

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels. - Massimiliano Patacchiola; Jack Turner; Elliot J. Crowley; Michael F. P. O'Boyle; Amos J. Storkey (NeurIPS, 2020)

Comparing recurrent and convolutional neural networks for predicting wave propagation. - Stathi Fotiadis; Eduardo Pignatelli; Mario Lino Valencia; Chris D. Cantwell; Amos J. Storkey; Anil A. Bharath (CoRR, 2020)

What Information Does a ResNet Compress? - Luke Nicholas Darlow; Amos J. Storkey (CoRR, 2020)

DHOG: Deep Hierarchical Object Grouping. - Luke Nicholas Darlow; Amos J. Storkey (CoRR, 2020)

Defining Benchmarks for Continual Few-Shot Learning. - Antreas Antoniou; Massimiliano Patacchiola; Mateusz Ochal; Amos J. Storkey (CoRR, 2020)

Neural Architecture Search without Training. - Joseph Charles Mellor; Jack Turner; Amos J. Storkey; Elliot J. Crowley (CoRR, 2020)

Constraint-Based Regularization of Neural Networks. - Benedict J. Leimkuhler; Timothée Pouchon; Tiffany Vlaar; Amos J. Storkey (CoRR, 2020)

Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons. - Paul Micaelli; Amos J. Storkey (CoRR, 2020)

Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks. - Luke Nicholas Darlow; Stanislaw Jastrzebski; Amos J. Storkey (CoRR, 2020)

Better Training using Weight-Constrained Stochastic Dynamics. - Benedict J. Leimkuhler; Tiffany J. Vlaar; Timothée Pouchon; Amos J. Storkey (ICML, 2021)

Neural Architecture Search without Training. - Joe Mellor; Jack Turner; Amos J. Storkey; Elliot J. Crowley (ICML, 2021)

Gradient-based Hyperparameter Optimization Over Long Horizons. - Paul Micaelli; Amos J. Storkey (NeurIPS, 2021)

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

How Sensitive are Meta-Learners to Dataset Imbalance? - Mateusz Ochal; Massimiliano Patacchiola; Amos J. Storkey; Jose Vazquez; Sen Wang 002 (CoRR, 2021)

Hamiltonian Operator Disentanglement of Content and Motion in Image Sequences. - Mohammad Asif Khan 001; Amos J. Storkey (CoRR, 2021)

Global explainability in aligned image modalities. - Justin Engelmann; Amos J. Storkey; Miguel O. Bernabeu (CoRR, 2021)

Prediction-Guided Distillation for Dense Object Detection. - Chenhongyi Yang; Mateusz Ochal; Amos J. Storkey; Elliot J. Crowley (ECCV (9), 2022)

Robust and Efficient Computation of Retinal Fractal Dimension Through Deep Approximation. - Justin Engelmann; Ana Villaplana-Velasco; Amos J. Storkey; Miguel O. Bernabeu (OMIA@MICCAI, 2022)

Hamiltonian Latent Operators for content and motion disentanglement in image sequences. - Asif Khan 008; Amos J. Storkey (NeurIPS, 2022)

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

Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning. - Lukas Schäfer 001; Filippos Christianos; Amos J. Storkey; Stefano V. Albrecht (CoRR, 2022)

Adversarial robustness of β-VAE through the lens of local geometry. - Asif Khan 008; Amos J. Storkey (CoRR, 2022)

Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols. - Bryan M. Li; Leonardo V. Castorina; Maria del C. Valdés Hernández; Una Clancy; Stewart J. Wiseman; Eleni Sakka; Amos J. Storkey; Daniela Jaime Garcia; Yajun Cheng; Fergus Doubal; Michael J. Thrippleton; Michael S. Stringer; et al. (Frontiers Comput. Neurosci., 2022)

Meta-Learning in Neural Networks: A Survey. - Timothy M. Hospedales; Antreas Antoniou; Paul Micaelli; Amos J. Storkey (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning. - Justin Engelmann; Alice D. McTrusty; Ian J. C. MacCormick; Emma Pead; Amos J. Storkey; Miguel O. Bernabeu (Nat. Mac. Intell., 2022)

Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry. - Alex Horton; Martin Ewart; Noel Gourmelen; Xavier Fettweis; Amos J. Storkey (Remote. Sens., 2022)

Adversarial robustness of VAEs through the lens of local geometry. - Asif Khan; Amos J. Storkey (AISTATS, 2023)

Contrastive Meta-Learning for Partially Observable Few-Shot Learning. - Adam Jelley; Amos J. Storkey; Antreas Antoniou; Sam Devlin (ICLR, 2023)

ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging. - Alessandro Fontanella; Antreas Antoniou; Wenwen Li 003; Joanna M. Wardlaw; Grant Mair; Emanuele Trucco; Amos J. Storkey (ICML, 2023)

QuickQual: Lightweight, Convenient Retinal Image Quality Scoring with Off-the-Shelf Pretrained Models. - Justin Engelmann; Amos J. Storkey; Miguel O. Bernabeu (OMIA@MICCAI, 2023)

Class Conditional Gaussians for Continual Learning. - Thomas L. Lee; Amos J. Storkey (CoRR, 2023)

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

Label Noise: Correcting a Correction. - William Toner; Amos J. Storkey (CoRR, 2023)

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

Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views. - Asif Khan; Amos J. Storkey (CoRR, 2023)

Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT. - Alessandro Fontanella; Wenwen Li 003; Grant Mair; Antreas Antoniou; Eleanor Platt; Paul A. Armitage; Emanuele Trucco; Joanna M. Wardlaw; Amos J. Storkey (CoRR, 2023)

Chunking: Forgetting Matters in Continual Learning even without Changing Tasks. - Thomas L. Lee; Amos J. Storkey (CoRR, 2023)

Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning. - Trevor McInroe; Stefano V. Albrecht; Amos J. Storkey (CoRR, 2023)

DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack. - Perry Gibson; José Cano 001; Elliot J. Crowley; Amos J. Storkey; Michael F. P. O'Boyle (CoRR, 2023)

Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography. - Justin Engelmann; Jamie Burke; Charlene Hamid; Megan Reid-Schachter; Dan Pugh; Neeraj Dhaun; Diana Moukaddem; Lyle Gray; Niall C. Strang; Paul McGraw; Amos J. Storkey; Paul J. Steptoe; et al. (CoRR, 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. - Joseph Mellor; Wenhua Jiang; Alan Fleming; Stuart J. McGurnaghan; Luke Blackbourn; Caroline Styles; Amos J. Storkey; Paul McKeigue; Helen M. Colhoun (Int. J. Medical Informatics, 2023)

Few-Shot Learning With Class Imbalance. - Mateusz Ochal; Massimiliano Patacchiola; Jose Vazquez; Amos J. Storkey; Sen Wang 002 (IEEE Trans. Artif. Intell., 2023)

Approximate Bayesian Class-Conditional Models under Continuous Representation Shift. - Thomas L. Lee; Amos J. Storkey (AISTATS, 2024)

Chunking: Continual Learning is not just about Distribution Shift. - Thomas L. Lee; Amos J. Storkey (CoLLAs, 2024)

DAM: Towards a Foundation Model for Forecasting. - Luke Nicholas Darlow; Qiwen Deng; Ahmed Hassan; Martin Asenov; Rajkarn Singh; Artjom Joosen; Adam Barker; Amos J. Storkey (ICLR, 2024)

Pre-processing and Quality Control of Large Clinical CT Head Datasets for Intracranial Arterial Calcification Segmentation. - Benjamin Jin; Maria del C. Valdés Hernández; Alessandro Fontanella; Wenwen Li 003; Eleanor Platt; Paul A. Armitage; Amos J. Storkey; Joanna M. Wardlaw; Grant Mair (DEMI@MICCAI, 2024)

Diffusion for World Modeling: Visual Details Matter in Atari. - Eloi Alonso; Adam Jelley; Vincent Micheli; Anssi Kanervisto; Amos J. Storkey; Tim Pearce; François Fleuret (NeurIPS, 2024)

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

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

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

Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images. - Jamie Burke; Justin Engelmann; Charlene Hamid; Diana Moukaddem; Dan Pugh; Neeraj Dhaun; Amos J. Storkey; Niall C. Strang; Stuart King; Tom J. MacGillivray; Miguel O. Bernabeu; Ian J. C. MacCormick (CoRR, 2024)

Efficient Offline Reinforcement Learning: The Critic is Critical. - Adam Jelley; Trevor McInroe; Sam Devlin; Amos J. Storkey (CoRR, 2024)

DAM: Towards A Foundation Model for Time Series Forecasting. - Luke Nicholas Darlow; Qiwen Deng; Ahmed Hassan; Martin Asenov; Rajkarn Singh; Artjom Joosen; Adam Barker; Amos J. Storkey (CoRR, 2024)

Noisy Early Stopping for Noisy Labels. - William Toner; Amos J. Storkey (CoRR, 2024)

LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots. - Dongge Han; Trevor McInroe; Adam Jelley; Stefano V. Albrecht; Peter Bell 001; Amos J. Storkey (COLING, 2025)

Objects matter: object-centric world models improve reinforcement learning in visually complex environments. - Weipu Zhang; Adam Jelley; Trevor McInroe; Amos J. Storkey (CoRR, 2025)

DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration. - Perry Gibson; José Cano 001; Elliot Crowley; Amos J. Storkey; Michael F. P. O'Boyle (ACM Trans. Archit. Code Optim., 2025)