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Amos J. Storkey
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- affiliation: University of Edinburgh, UK
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2020 – today
- 2024
- [j21]Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Amos J. Storkey:
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning. RLJ 2: 516-546 (2024) - [c69]Thomas L. Lee, Amos J. Storkey:
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift. AISTATS 2024: 3628-3636 - [c68]Luke Nicholas Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker, Amos J. Storkey:
DAM: Towards a Foundation Model for Forecasting. ICLR 2024 - [c67]Benjamin Jin
, Maria del C. Valdés Hernández
Pre-processing and Quality Control of Large Clinical CT Head Datasets for Intracranial Arterial Calcification Segmentation. DEMI@MICCAI 2024: 73-83 - [i72]Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson, Elliot J. Crowley, Amos J. Storkey:
Hyperparameter Selection in Continual Learning. CoRR abs/2404.06466 (2024) - [i71]Dongge Han, Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Peter Bell, Amos J. Storkey:
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots. CoRR abs/2404.14285 (2024) - [i70]Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos J. Storkey, Tim Pearce, François Fleuret:
Diffusion for World Modeling: Visual Details Matter in Atari. CoRR abs/2405.12399 (2024) - [i69]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:
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images. CoRR abs/2405.14453 (2024) - [i68]Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos J. Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley:
einspace: Searching for Neural Architectures from Fundamental Operations. CoRR abs/2405.20838 (2024) - [i67]Adam Jelley, Trevor McInroe, Sam Devlin, Amos J. Storkey:
Efficient Offline Reinforcement Learning: The Critic is Critical. CoRR abs/2406.13376 (2024) - [i66]Luke Nicholas Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker, Amos J. Storkey:
DAM: Towards A Foundation Model for Time Series Forecasting. CoRR abs/2407.17880 (2024) - [i65]William Toner, Amos J. Storkey:
Noisy Early Stopping for Noisy Labels. CoRR abs/2409.06830 (2024) - 2023
- [j20]Joseph Mellor
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. Int. J. Medical Informatics 175: 105072 (2023) - [j19]Mateusz Ochal
Few-Shot Learning With Class Imbalance. IEEE Trans. Artif. Intell. 4(5): 1348-1358 (2023) - [c66]Asif Khan, Amos J. Storkey:
Adversarial robustness of VAEs through the lens of local geometry. AISTATS 2023: 8954-8967 - [c65]Adam Jelley, Amos J. Storkey, Antreas Antoniou, Sam Devlin:
Contrastive Meta-Learning for Partially Observable Few-Shot Learning. ICLR 2023 - [c64]Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna M. Wardlaw, Grant Mair, Emanuele Trucco, Amos J. Storkey:
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging. ICML 2023: 10153-10169 - [c63]Justin Engelmann, Amos J. Storkey, Miguel O. Bernabeu:
QuickQual: Lightweight, Convenient Retinal Image Quality Scoring with Off-the-Shelf Pretrained Models. OMIA@MICCAI 2023: 32-41 - [i64]Adam Jelley, Amos J. Storkey, Antreas Antoniou, Sam Devlin:
Contrastive Meta-Learning for Partially Observable Few-Shot Learning. CoRR abs/2301.13136 (2023) - [i63]Alessandro Fontanella, Antreas Antoniou, Wenwen Li, Joanna M. Wardlaw, Grant Mair
ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging. CoRR abs/2303.15421 (2023) - [i62]Thomas L. Lee, Amos J. Storkey:
Class Conditional Gaussians for Continual Learning. CoRR abs/2305.19076 (2023) - [i61]Jamie Burke
Efficient and fully-automatic retinal choroid segmentation in OCT through DL-based distillation of a hand-crafted pipeline. CoRR abs/2307.00904 (2023) - [i60]William Toner, Amos J. Storkey:
Label Noise: Correcting a Correction. CoRR abs/2307.13100 (2023) - [i59]Justin Engelmann, Amos J. Storkey, Miguel O. Bernabeu:
QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models. CoRR abs/2307.13646 (2023) - [i58]Alessandro Fontanella, Grant Mair
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images. CoRR abs/2308.02062 (2023) - [i57]Asif Khan, Amos J. Storkey:
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views. CoRR abs/2308.10077 (2023) - [i56]Alessandro Fontanella, Wenwen Li, Grant Mair
Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT. CoRR abs/2309.17320 (2023) - [i55]Thomas L. Lee, Amos J. Storkey:
Chunking: Forgetting Matters in Continual Learning even without Changing Tasks. CoRR abs/2310.02206 (2023) - [i54]Trevor McInroe, Stefano V. Albrecht, Amos J. Storkey:
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning. CoRR abs/2310.05723 (2023) - [i53]Perry Gibson, José Cano, Elliot J. Crowley, Amos J. Storkey, Michael F. P. O'Boyle:
DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack. CoRR abs/2311.08909 (2023) - [i52]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, Stuart King, Tom J. MacGillivray, Miguel O. Bernabeu, Ian J. C. MacCormick:
Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography. CoRR abs/2312.02956 (2023) - 2022
- [j18]Bryan M. Li
Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols. Frontiers Comput. Neurosci. 16 (2022) - [j17]Justin Engelmann
Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning. Nat. Mac. Intell. 4(12): 1143-1154 (2022) - [j16]Timothy M. Hospedales
Meta-Learning in Neural Networks: A Survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 5149-5169 (2022) - [j15]Alex Horton, Martin Ewart, Noel Gourmelen
Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry. Remote. Sens. 14(24): 6210 (2022) - [c62]Chenhongyi Yang, Mateusz Ochal
Prediction-Guided Distillation for Dense Object Detection. ECCV (9) 2022: 123-138 - [c61]Justin Engelmann, Ana Villaplana-Velasco, Amos J. Storkey, Miguel O. Bernabeu:
Robust and Efficient Computation of Retinal Fractal Dimension Through Deep Approximation. OMIA@MICCAI 2022: 84-93 - [c60]Asif Khan, Amos J. Storkey:
Hamiltonian Latent Operators for content and motion disentanglement in image sequences. NeurIPS 2022 - [i51]Chenhongyi Yang, Mateusz Ochal
Prediction-Guided Distillation for Dense Object Detection. CoRR abs/2203.05469 (2022) - [i50]Justin Engelmann, Alice D. McTrusty, Ian J. C. MacCormick, Emma Pead, Amos J. Storkey, Miguel O. Bernabeu:
Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions. CoRR abs/2203.06113 (2022) - [i49]Lukas Schäfer, Filippos Christianos, Amos J. Storkey, Stefano V. Albrecht:
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning. CoRR abs/2207.02249 (2022) - [i48]Justin Engelmann, Ana Villaplana-Velasco, Amos J. Storkey, Miguel O. Bernabeu:
Robust and efficient computation of retinal fractal dimension through deep approximation. CoRR abs/2207.05757 (2022) - [i47]Asif Khan, Amos J. Storkey:
Adversarial robustness of β-VAE through the lens of local geometry. CoRR abs/2208.03923 (2022) - 2021
- [j14]Elliot J. Crowley
Substituting Convolutions for Neural Network Compression. IEEE Access 9: 83199-83213 (2021) - [c59]Benedict J. Leimkuhler, Tiffany J. Vlaar, Timothée Pouchon, Amos J. Storkey:
Better Training using Weight-Constrained Stochastic Dynamics. ICML 2021: 6200-6211 - [c58]Joe Mellor, Jack Turner, Amos J. Storkey, Elliot J. Crowley:
Neural Architecture Search without Training. ICML 2021: 7588-7598 - [c57]Paul Micaelli, Amos J. Storkey:
Gradient-based Hyperparameter Optimization Over Long Horizons. NeurIPS 2021: 10798-10809 - [i46]Mateusz Ochal, Massimiliano Patacchiola, Amos J. Storkey, Jose Vazquez, Sen Wang:
Few-Shot Learning with Class Imbalance. CoRR abs/2101.02523 (2021) - [i45]Mateusz Ochal, Massimiliano Patacchiola, Amos J. Storkey, Jose Vazquez, Sen Wang:
How Sensitive are Meta-Learners to Dataset Imbalance? CoRR abs/2104.05344 (2021) - [i44]Benedict J. Leimkuhler, Tiffany Vlaar, Timothée Pouchon, Amos J. Storkey:
Better Training using Weight-Constrained Stochastic Dynamics. CoRR abs/2106.10704 (2021) - [i43]Mohammad Asif Khan, Amos J. Storkey:
Hamiltonian Operator Disentanglement of Content and Motion in Image Sequences. CoRR abs/2112.01641 (2021) - [i42]Justin Engelmann, Amos J. Storkey, Miguel O. Bernabeu:
Global explainability in aligned image modalities. CoRR abs/2112.09591 (2021) - 2020
- [c56]Perry Gibson
Optimizing Grouped Convolutions on Edge Devices. ASAP 2020: 189-196 - [c55]Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle, Amos J. Storkey, Gavin Gray:
BlockSwap: Fisher-guided Block Substitution for Network Compression on a Budget. ICLR 2020 - [c54]Massimiliano Patacchiola, Amos J. Storkey:
Self-Supervised Relational Reasoning for Representation Learning. NeurIPS 2020 - [c53]Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle, Amos J. Storkey:
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels. NeurIPS 2020 - [i41]Valentin Radu, Kuba Kaszyk
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs. CoRR abs/2002.08697 (2020) - [i40]Stathi Fotiadis, Eduardo Pignatelli, Mario Lino Valencia, Chris D. Cantwell, Amos J. Storkey, Anil A. Bharath:
Comparing recurrent and convolutional neural networks for predicting wave propagation. CoRR abs/2002.08981 (2020) - [i39]Luke Nicholas Darlow, Amos J. Storkey:
What Information Does a ResNet Compress? CoRR abs/2003.06254 (2020) - [i38]Luke Nicholas Darlow, Amos J. Storkey:
DHOG: Deep Hierarchical Object Grouping. CoRR abs/2003.08821 (2020) - [i37]Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, Amos J. Storkey:
Meta-Learning in Neural Networks: A Survey. CoRR abs/2004.05439 (2020) - [i36]Antreas Antoniou, Massimiliano Patacchiola, Mateusz Ochal, Amos J. Storkey:
Defining Benchmarks for Continual Few-Shot Learning. CoRR abs/2004.11967 (2020) - [i35]Joseph Charles Mellor, Jack Turner, Amos J. Storkey, Elliot J. Crowley:
Neural Architecture Search without Training. CoRR abs/2006.04647 (2020) - [i34]Massimiliano Patacchiola, Amos J. Storkey:
Self-Supervised Relational Reasoning for Representation Learning. CoRR abs/2006.05849 (2020) - [i33]Perry Gibson, José Cano, Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle, Amos J. Storkey:
Optimizing Grouped Convolutions on Edge Devices. CoRR abs/2006.09791 (2020) - [i32]Benedict J. Leimkuhler, Timothée Pouchon, Tiffany Vlaar, Amos J. Storkey:
Constraint-Based Regularization of Neural Networks. CoRR abs/2006.10114 (2020) - [i31]Paul Micaelli, Amos J. Storkey:
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons. CoRR abs/2007.07869 (2020) - [i30]Luke Nicholas Darlow, Stanislaw Jastrzebski, Amos J. Storkey:
Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks. CoRR abs/2011.11486 (2020)
2010 – 2019
- 2019
- [c52]Antreas Antoniou, Harrison Edwards, Amos J. Storkey:
How to train your MAML. ICLR (Poster) 2019 - [c51]Yuri Burda, Harri Edwards, Deepak Pathak, Amos J. Storkey, Trevor Darrell, Alexei A. Efros:
Large-Scale Study of Curiosity-Driven Learning. ICLR (Poster) 2019 - [c50]Yuri Burda, Harrison Edwards, Amos J. Storkey, Oleg Klimov:
Exploration by random network distillation. ICLR (Poster) 2019 - [c49]Stanislaw Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length. ICLR (Poster) 2019 - [c48]Valentin Radu
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs. IISWC 2019: 24-34 - [c47]Paul Micaelli, Amos J. Storkey:
Zero-shot Knowledge Transfer via Adversarial Belief Matching. NeurIPS 2019: 9547-9557 - [c46]Antreas Antoniou, Amos J. Storkey:
Learning to Learn By Self-Critique. NeurIPS 2019: 9936-9946 - [i29]Antreas Antoniou, Amos J. Storkey:
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation. CoRR abs/1902.09884 (2019) - [i28]Paul Micaelli, Amos J. Storkey:
Zero-shot Knowledge Transfer via Adversarial Belief Matching. CoRR abs/1905.09768 (2019) - [i27]Antreas Antoniou, Amos J. Storkey:
Learning to learn via Self-Critique. CoRR abs/1905.10295 (2019) - [i26]Gavin Gray, Elliot J. Crowley, Amos J. Storkey:
Separable Layers Enable Structured Efficient Linear Substitutions. CoRR abs/1906.00859 (2019) - [i25]Jack Turner, Elliot J. Crowley, Gavin Gray, Amos J. Storkey, Michael F. P. O'Boyle:
BlockSwap: Fisher-guided Block Substitution for Network Compression. CoRR abs/1906.04113 (2019) - [i24]Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Amos J. Storkey:
Deep Kernel Transfer in Gaussian Processes for Few-shot Learning. CoRR abs/1910.05199 (2019) - 2018
- [c45]Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer
Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio. ICANN (3) 2018: 392-402 - [c44]Antreas Antoniou, Amos J. Storkey, Harrison Edwards:
Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks. ICANN (3) 2018: 594-603 - [c43]Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
Finding Flatter Minima with SGD. ICLR (Workshop) 2018 - [c42]Jack Turner, José Cano
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks. IISWC 2018: 101-110 - [c41]Elliot J. Crowley, Gavin Gray, Amos J. Storkey:
Moonshine: Distilling with Cheap Convolutions. NeurIPS 2018: 2893-2903 - [e1]Amos J. Storkey, Fernando Pérez-Cruz:
International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain. Proceedings of Machine Learning Research 84, PMLR 2018 [contents] - [i23]Stanislaw Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
DNN's Sharpest Directions Along the SGD Trajectory. CoRR abs/1807.05031 (2018) - [i22]Yuri Burda, Harri Edwards, Deepak Pathak, Amos J. Storkey, Trevor Darrell, Alexei A. Efros:
Large-Scale Study of Curiosity-Driven Learning. CoRR abs/1808.04355 (2018) - [i21]Jack Turner, José Cano, Valentin Radu, Elliot J. Crowley, Michael F. P. O'Boyle, Amos J. Storkey:
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks. CoRR abs/1809.07196 (2018) - [i20]Luke Nicholas Darlow, Amos J. Storkey:
GINN: Geometric Illustration of Neural Networks. CoRR abs/1810.01860 (2018) - [i19]Luke Nicholas Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. Storkey:
CINIC-10 is not ImageNet or CIFAR-10. CoRR abs/1810.03505 (2018) - [i18]Elliot J. Crowley, Jack Turner, Amos J. Storkey, Michael F. P. O'Boyle:
Pruning neural networks: is it time to nip it in the bud? CoRR abs/1810.04622 (2018) - [i17]Antreas Antoniou, Harrison Edwards, Amos J. Storkey:
How to train your MAML. CoRR abs/1810.09502 (2018) - [i16]Jack Turner, Elliot J. Crowley, Valentin Radu, José Cano, Amos J. Storkey, Michael F. P. O'Boyle:
HAKD: Hardware Aware Knowledge Distillation. CoRR abs/1810.10460 (2018) - [i15]Yuri Burda, Harrison Edwards, Amos J. Storkey, Oleg Klimov:
Exploration by Random Network Distillation. CoRR abs/1810.12894 (2018) - [i14]Antreas Antoniou, Agnieszka Slowik
Dilated DenseNets for Relational Reasoning. CoRR abs/1811.00410 (2018) - 2017
- [c40]Matthew M. Graham, Amos J. Storkey:
Asymptotically exact inference in differentiable generative models. AISTATS 2017: 499-508 - [c39]Tim Llewellynn, Maria del Milagro Fernández-Carrobles, Oscar Déniz
BONSEYES: Platform for Open Development of Systems of Artificial Intelligence: Invited paper. Conf. Computing Frontiers 2017: 299-304 - [c38]Harrison Edwards, Amos J. Storkey:
Towards a Neural Statistician. ICLR (Poster) 2017 - [c37]Matthew M. Graham, Amos J. Storkey:
Continuously Tempered Hamiltonian Monte Carlo. UAI 2017 - [i13]Elliot J. Crowley, Gavin Gray, Amos J. Storkey:
Moonshine: Distilling with Cheap Convolutions. CoRR abs/1711.02613 (2017) - [i12]Antreas Antoniou
Data Augmentation Generative Adversarial Networks. CoRR abs/1711.04340 (2017) - [i11]Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
Three Factors Influencing Minima in SGD. CoRR abs/1711.04623 (2017) - 2016
- [j13]Cyril R. Pernet
Evaluation of a pre-surgical functional MRI workflow: From data acquisition to reporting. Int. J. Medical Informatics 86: 37-42 (2016) - [c36]Zhanxing Zhu, Amos J. Storkey:
Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems. AAAI 2016: 2429-2437 - [c35]Harrison Edwards, Amos J. Storkey:
Censoring Representations with an Adversary. ICLR (Poster) 2016 - [i10]Harrison Edwards, Amos J. Storkey:
Towards a Neural Statistician. CoRR abs/1606.02185 (2016) - 2015
- [j12]Andrew M. Dai, Amos J. Storkey:
The Supervised Hierarchical Dirichlet Process. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 243-255 (2015) - [c34]Christopher Clark, Amos J. Storkey:
Training Deep Convolutional Neural Networks to Play Go. ICML 2015: 1766-1774 - [c33]Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey:
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. NIPS 2015: 37-45 - [c32]Amos J. Storkey, Zhanxing Zhu, Jinli Hu:
Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets. ECML/PKDD (1) 2015: 560-574 - [c31]Zhanxing Zhu, Amos J. Storkey:
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. ECML/PKDD (1) 2015: 645-658 - [i9]Zhanxing Zhu, Amos J. Storkey:
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. CoRR abs/1506.04093 (2015) - [i8]Xiaocheng Shang, Zhanxing Zhu, Benedict J. Leimkuhler, Amos J. Storkey:
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. CoRR abs/1510.08692 (2015) - 2014
- [j11]Colin R. Buchanan, Cyril R. Pernet
Test-retest reliability of structural brain networks from diffusion MRI. NeuroImage 86: 231-243 (2014) - [j10]Simon M. J. Lyons, Simo Särkkä, Amos J. Storkey:
Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes. IEEE Trans. Signal Process. 62(6): 1514-1524 (2014) - [c30]Jinli Hu, Amos J. Storkey:
Multi-period Trading Prediction Markets with Connections to Machine Learning. ICML 2014: 1773-1781 - [i7]Jinli Hu, Amos J. Storkey:
Multi-period Trading Prediction Markets with Connections to Machine Learning. CoRR abs/1403.0648 (2014) - [i6]Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann:
Renewal Strings for Cleaning Astronomical Databases. CoRR abs/1408.1489 (2014) - [i5]Christopher Clark, Amos J. Storkey:
Teaching Deep Convolutional Neural Networks to Play Go. CoRR abs/1412.3409 (2014) - [i4]Andrew M. Dai, Amos J. Storkey:
The supervised hierarchical Dirichlet process. CoRR abs/1412.5236 (2014) - 2013
- [j9]Krzysztof J. Gorgolewski
Single subject fMRI test-retest reliability metrics and confounding factors. NeuroImage 69: 231-243 (2013) - [j8]David P. Reichert, Peggy Seriès
Charles Bonnet Syndrome: Evidence for a Generative Model in the Cortex? PLoS Comput. Biol. 9(7) (2013) - [i3]Amos J. Storkey:
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules. CoRR abs/1301.3895 (2013) - [i2]Peter Orchard
Bayesian Inference in Sparse Gaussian Graphical Models. CoRR abs/1309.7311 (2013) - 2012
- [c29]Athina Spiliopoulou, Amos J. Storkey:
A Topic Model for Melodic Sequences. ICML 2012 - [c28]Amos J. Storkey, Jono Millin, Krzysztof J. Geras:
Isoelastic Agents and Wealth Updates in Machine Learning Markets. ICML 2012 - [c27]Simon M. J. Lyons, Amos J. Storkey, Simo Särkkä:
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes. NIPS 2012: 1961-1969 - [c26]Yichuan Zhang, Charles Sutton, Amos J. Storkey, Zoubin Ghahramani:
Continuous Relaxations for Discrete Hamiltonian Monte Carlo. NIPS 2012: 3203-3211 - [c25]Felix V. Agakov, Peter Orchard
Discriminative Mixtures of Sparse Latent Fields for Risk Management. AISTATS 2012: 10-18 - 2011
- [j7]Lawrence Murray
Particle Smoothing in Continuous Time: A Fast Approach via Density Estimation. IEEE Trans. Signal Process. 59(3): 1017-1026 (2011) - [c24]David P. Reichert, Peggy Seriès, Amos J. Storkey:
A Hierarchical Generative Model of Recurrent Object-Based Attention in the Visual Cortex. ICANN (1) 2011: 18-25 - [c23]Andrew M. Dai, Amos J. Storkey:
The Grouped Author-Topic Model for Unsupervised Entity Resolution. ICANN (1) 2011: 241-249 - [c22]David P. Reichert, Peggy Seriès, Amos J. Storkey:
Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability. NIPS 2011: 2357-2365 - [c21]Athina Spiliopoulou, Amos J. Storkey:
Comparing Probabilistic Models for Melodic Sequences. ECML/PKDD (3) 2011: 289-304 - [c20]Amos J. Storkey:
Machine Learning Markets. AISTATS 2011: 716-724 - [i1]Amos J. Storkey:
Machine Learning Markets. CoRR abs/1106.4509 (2011) - 2010
- [c19]Felix V. Agakov, Paul McKeigue, Jon Krohn, Amos J. Storkey:
Sparse Instrumental Variables (SPIV) for Genome-Wide Studies. NIPS 2010: 28-36 - [c18]David P. Reichert, Peggy Seriès, Amos J. Storkey:
Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model. NIPS 2010: 2020-2028
2000 – 2009
- 2009
- [j6]Jonathan D. Clayden
Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach. NeuroImage 45(2): 377-385 (2009) - 2008
- [j5]Mark E. Bastin
Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing. NeuroImage 43(1): 20-28 (2008) - 2007
- [j4]Jonathan D. Clayden, Amos J. Storkey, Mark E. Bastin
A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation. IEEE Trans. Medical Imaging 26(11): 1555-1561 (2007) - [c17]Ben H. Williams, Marc Toussaint, Amos J. Storkey:
A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data. IJCAI 2007: 1119-1124 - [c16]Lawrence Murray, Amos J. Storkey:
Continuous Time Particle Filtering for fMRI. NIPS 2007: 1049-1056 - [c15]Ben H. Williams, Marc Toussaint, Amos J. Storkey:
Modelling motion primitives and their timing in biologically executed movements. NIPS 2007: 1609-1616 - 2006
- [j3]Jonathan D. Clayden, Mark E. Bastin
Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure. NeuroImage 33(2): 482-492 (2006) - [c14]Ben H. Williams, Marc Toussaint, Amos J. Storkey:
Extracting Motion Primitives from Natural Handwriting Data. ICANN (2) 2006: 634-643 - [c13]Marc Toussaint, Amos J. Storkey:
Probabilistic inference for solving discrete and continuous state Markov Decision Processes. ICML 2006: 945-952 - [c12]Amos J. Storkey, Enrico Simonotto, Heather Whalley, Stephen M. Lawrie, Lawrence Murray, David J. McGonigle:
Learning Structural Equation Models for fMRI. NIPS 2006: 1329-1336 - [c11]Amos J. Storkey, Masashi Sugiyama:
Mixture Regression for Covariate Shift. NIPS 2006: 1337-1344 - 2005
- [c10]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, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen
The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176 - 2004
- [c9]Amos J. Storkey, Michael Allan:
Cosine Transform Priors for Enhanced Decoding of Compressed Images. IDEAL 2004: 533-539 - 2003
- [j2]Amos J. Storkey, Christopher K. I. Williams:
Image Modeling with Position-Encoding Dynamic Trees. IEEE Trans. Pattern Anal. Mach. Intell. 25(7): 859-871 (2003) - [c8]Amos J. Storkey:
Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data. NIPS 2003: 433-440 - [c7]Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann:
Renewal Strings for Cleaning Astronomical Databases. UAI 2003: 559-566 - 2002
- [c6]Amos J. Storkey:
Dynamic Structure Super-Resolution. NIPS 2002: 1295-1302 - 2001
- [c5]Amos J. Storkey, Christopher K. I. Williams:
Dynamic Positional Trees for Structural Image Analysis. AISTATS 2001: 286-292 - 2000
- [c4]Nicholas J. Adams, Amos J. Storkey, Christopher K. I. Williams, Zoubin Ghahramani:
MFDTs: Mean Field Dynamic Trees. ICPR 2000: 3151-3154 - [c3]Amos J. Storkey:
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules. UAI 2000: 566-573
1990 – 1999
- 1999
- [j1]Amos J. Storkey, Romain Valabrègue
The basins of attraction of a new Hopfield learning rule. Neural Networks 12(6): 869-876 (1999) - 1997
- [c2]Amos J. Storkey:
Increasing the Capacity of a Hopfield Network without Sacrificing Functionality. ICANN 1997: 451-456 - 1996
- [c1]Christopher John Browne, Joel de L. Pereira Castro Jr., Amos J. Storkey:
A Modified Spreading Algorithm for Autoassociation in Weightless Neural Networks. ICANN 1996: 569-574
Coauthor Index
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