Amos Storkey
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British machine learning academic
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Amos Storkeycomputer-science Degrees
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Why Is Amos Storkey Influential?
(Suggest an Edit or Addition)According to Wikipedia, Amos James Storkey is Professor of Machine Learning and Artificial Intelligence at the School of Informatics, University of Edinburgh. Storkey studied mathematics at Trinity College, Cambridge and obtained his doctorate from Imperial College, London. In 1997 during his PhD, he worked on the Hopfield Network a form of recurrent artificial neural network popularized by John Hopfield in 1982. Hopfield nets serve as content-addressable memory systems with binary threshold nodes and Storkey developed what became known as the "Storkey Learning Rule".
Amos Storkey's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Meta-Learning in Neural Networks: A Survey (2020) (850)
- Exploration by Random Network Distillation (2018) (801)
- Data Augmentation Generative Adversarial Networks (2017) (758)
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16 (2004) (683)
- How to train your MAML (2018) (532)
- Large-Scale Study of Curiosity-Driven Learning (2018) (529)
- Probabilistic inference for solving discrete and continuous state Markov Decision Processes (2006) (514)
- The 2005 PASCAL Visual Object Classes Challenge (2005) (443)
- Censoring Representations with an Adversary (2015) (415)
- Towards a Neural Statistician (2016) (357)
- Three Factors Influencing Minima in SGD (2017) (355)
- Extracting Motion Primitives from Natural Handwriting Data (2006) (253)
- CINIC-10 is not ImageNet or CIFAR-10 (2018) (209)
- Training Deep Convolutional Neural Networks to Play Go (2014) (177)
- Neural Architecture Search without Training (2020) (170)
- Test–retest reliability of structural brain networks from diffusion MRI (2014) (139)
- When Training and Test Sets are Different: Characterising Learning Transfer (2013) (134)
- Zero-shot Knowledge Transfer via Adversarial Belief Matching (2019) (122)
- Probabilistic inference for solving (PO) MDPs (2006) (109)
- TractoR: Magnetic Resonance Imaging and Tractography with R (2011) (105)
- Mixture Regression for Covariate Shift (2006) (103)
- Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks (2018) (97)
- Moonshine: Distilling with Cheap Convolutions (2017) (94)
- Single subject fMRI test–retest reliability metrics and confounding factors (2013) (90)
- The basins of attraction of a new Hopfield learning rule (1999) (89)
- Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels (2020) (81)
- On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length (2018) (80)
- A Probabilistic Model-Based Approach to Consistent White Matter Tract Segmentation (2007) (76)
- Automating Morphological Profiling with Generic Deep Convolutional Networks (2016) (68)
- Increasing the Capacity of a Hopfield Network without Sacrificing Functionality (1997) (68)
- Brain white matter structure and information processing speed in healthy older age (2015) (65)
- Image Modeling with Position-Encoding Dynamic Trees (2003) (60)
- Modelling motion primitives and their timing in biologically executed movements (2007) (59)
- Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation (2019) (58)
- Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features (2017) (58)
- Adaptive thresholding for reliable topological inference in single subject fMRI analysis (2012) (53)
- Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling (2015) (52)
- Charles Bonnet Syndrome: Evidence for a Generative Model in the Cortex? (2013) (48)
- Continuous Relaxations for Discrete Hamiltonian Monte Carlo (2012) (47)
- A test-retest fMRI dataset for motor, language and spatial attention functions (2013) (43)
- Machine Learning Markets (2011) (42)
- Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach (2009) (40)
- The Supervised Hierarchical Dirichlet Process (2014) (37)
- Self-Supervised Relational Reasoning for Representation Learning (2020) (36)
- Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks (2018) (35)
- Cleaning sky survey data bases using Hough transform and renewal string approaches (2003) (32)
- Truncated covariance matrices and Toeplitz methods in Gaussian processes (1999) (32)
- Defining Benchmarks for Continual Few-Shot Learning (2020) (32)
- Reduced structural connectivity within a prefrontal‐motor‐subcortical network in amyotrophic lateral sclerosis (2015) (31)
- Asymptotically exact inference in differentiable generative models (2016) (30)
- Dynamic Structure Super-Resolution (2002) (28)
- Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model (2010) (28)
- Neural Information Processing Systems (NIPS) (2015) (28)
- Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs (2019) (28)
- Improved segmentation reproducibility in group tractography using a quantitative tract similarity measure (2006) (28)
- BONSEYES: Platform for Open Development of Systems of Artificial Intelligence: Invited paper (2017) (28)
- Finding Flatter Minima with SGD (2018) (27)
- Comparing recurrent and convolutional neural networks for predicting wave propagation (2020) (27)
- Particle Smoothing in Continuous Time: A Fast Approach via Density Estimation (2011) (27)
- Advances in Neural Information Processing Systems 25 (NIPS 2012) (2002) (27)
- Pruning neural networks: is it time to nip it in the bud? (2018) (27)
- Learning to Learn By Self-Critique (2019) (25)
- The Grouped Author-Topic Model for Unsupervised Entity Resolution (2011) (24)
- Continuous Time Particle Filtering for fMRI (2007) (24)
- A Closer Look at Structured Pruning for Neural Network Compression (2018) (23)
- Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing (2008) (23)
- Continuously Tempered Hamiltonian Monte Carlo (2016) (22)
- Isoelastic Agents and Wealth Updates in Machine Learning Markets (2012) (22)
- Individualized prediction of psychosis in subjects with an at-risk mental state (2017) (22)
- Multi-period Trading Prediction Markets with Connections to Machine Learning (2014) (20)
- Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems (2015) (20)
- Efficient Covariance Matrix Methods for Bayesian Gaussian Processes and Hopfield Neural Networks (1999) (19)
- Machine Learning and Pattern Recognition : Preliminaries Course (2009) (19)
- Optimizing Grouped Convolutions on Edge Devices (2020) (18)
- Hopfield learning rule with high capacity storage of time-correlated patterns (1997) (18)
- MFDTs: mean field dynamic trees (2000) (18)
- A Hierarchical Generative Model of Recurrent Object-Based Attention in the Visual Cortex (2011) (18)
- Deep Kernel Transfer in Gaussian Processes for Few-shot Learning (2019) (18)
- Advances in Neural Information Processing Systems 19 (NIPS 2006) (2006) (18)
- A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data (2007) (18)
- 18th Annual Meeting of the Organization for Human Brain Mapping (2012) (18)
- Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio (2018) (17)
- An Expectation Maximisation Algorithm for One-to-Many Record Linkage, Illustrated on the Problem of Matching Far Infra-Red Astronomical Sources to Optical Counterparts (2005) (17)
- Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (2016) (16)
- Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules (2000) (16)
- Learning to learn via Self-Critique (2019) (16)
- In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (2001) (16)
- Learning Structural Equation Models for fMRI (2006) (14)
- Proceedings of NIPS Workshop on Applications for Topic Models Text and Beyond (2009) (13)
- Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons (2020) (13)
- AMIA 2007 Symposium Proceedings (2006) (13)
- Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes (2013) (13)
- Palimpsest memories: a new high-capacity forgetful learning rule for Hopfield networks (1998) (12)
- Author Disambiguation: A Nonparametric Topic andCo-authorship Model (2009) (12)
- Discriminative Mixtures of Sparse Latent Fields for Risk Management (2012) (12)
- Bayesian Time Series Models: Expectation maximisation methods for solving (PO)MDPs and optimal control problems (2011) (11)
- Sparse Instrumental Variables (SPIV) for Genome-Wide Studies (2010) (11)
- Comparing Probabilistic Models for Melodic Sequences (2011) (11)
- Generative Model (2018) (10)
- Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability (2011) (10)
- Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks (2020) (9)
- Bayesian Inference in Sparse Gaussian Graphical Models (2013) (9)
- UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence (2000) (8)
- Few-Shot Learning with Class Imbalance (2021) (8)
- A Topic Model for Melodic Sequences (2012) (8)
- The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes (2012) (8)
- Generalised Propagation for Fast Fourier Transforms with Partial or Missing Data (2003) (7)
- When Training and Test Sets Are Different (2008) (7)
- Evaluation of a pre-surgical functional MRI workflow: From data acquisition to reporting (2016) (7)
- Stochastic Parallel Block Coordinate Descent for Large-Scale Saddle Point Problems (2015) (7)
- What Information Does a ResNet Compress? (2020) (7)
- Dynamic Positional Trees for Structural Image Analysis (2001) (6)
- Scientific DataMining, Integration, and Visualization (2002) (6)
- Constraint-Based Regularization of Neural Networks (2020) (6)
- BlockSwap: Fisher-guided Block Substitution for Network Compression (2019) (6)
- Distilling with Performance Enhanced Students (2018) (6)
- Predicting ambulance diversion in an adult Emergency Department using a Gaussian process. (2007) (6)
- DHOG: Deep Hierarchical Object Grouping (2020) (6)
- Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012 (2012) (5)
- Renewal Strings for Cleaning Astronomical Databases (2002) (5)
- Chapter 1 Expectation-Maximization methods for solving ( PO ) MDPs and optimal control problems (2009) (5)
- Aggregation Under Bias: Rényi Divergence Aggregation and Its Implementation via Machine Learning Markets (2015) (5)
- Gradient-based Hyperparameter Optimization Over Long Horizons (2020) (5)
- Comparing Mean Field and Exact EM in Tree Structured Belief Networks (2001) (4)
- Report of workshop held at the National e-Science Institute (2002) (4)
- Separable Layers Enable Structured Efficient Linear Substitutions (2019) (4)
- In Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry. ICSC-NAISO Adademic (2001) (4)
- Prediction-Guided Distillation for Dense Object Detection (2022) (4)
- Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12) (2012) (3)
- Pitfalls of Thresholding Statistical Maps in Presurgical fMRI Mapping (2011) (3)
- Better Training using Weight-Constrained Stochastic Dynamics (2021) (3)
- HAKD: Hardware Aware Knowledge Distillation (2018) (3)
- Introduction Machine Learning and Pattern Recognition (2014) (3)
- THE SURROGATE SCIENCES (1990) (3)
- Toward real-world automated antibody design with combinatorial Bayesian optimization (2023) (3)
- Learning from Data 1 Naive Bayes (2002) (3)
- Asymptotically exact inference in likelihood-free models (2016) (2)
- SGD Smooths The Sharpest Directions (2018) (2)
- Cosine Transform Priors for Enhanced Decoding of Compressed Images (2004) (2)
- Probabilistic inference for computing optimal policies in MDPs (2005) (2)
- Dilated DenseNets for Relational Reasoning (2018) (2)
- Gaussian Processes for Switching Regimes (1998) (2)
- Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning (2022) (2)
- Asymptotically exact conditional inference in deep generative models and differentiable simulators (2016) (2)
- Using a Combination of a Mixture Model and Topological FDR in the Context of Presurgical Planning (2011) (2)
- Automated assessment of tract similarity in group diffusion MRI data (2006) (1)
- The Context-Aware Learner (2018) (1)
- How Sensitive are Meta-Learners to Dataset Imbalance? (2021) (1)
- Curiosity-driven Exploration by Bootstrapping Features (2018) (1)
- A Continuum from Mixtures to Products : Aggregation under Bias (2014) (1)
- Quantifying the intra- and inter-subject variability of whole-brain structural networks from diffusion MRI (2012) (1)
- Global explainability in aligned image modalities (2021) (1)
- Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. (2022) (1)
- Discovering white matter structure beyond fractional anisotropy maps (2009) (1)
- Learning from Data. Density Estimation: Gaussians (2005) (1)
- Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning (2022) (1)
- Reliability of single subject fMRI in the context of presurgical planning (2012) (1)
- A Modified Spreading Algorithm for Autoassociation in Weightless Neural Networks (1996) (1)
- The Fractal and Multifractal Nature of Traffic (2007) (1)
- Segmenting white matter structure from diffusion MRI (2012) (0)
- Poster #M160 IMPROVED INDIVIDUALISED PREDICTION OF SCHIZOPHRENIA IN SUBJECTS AT GENETIC HIGH RISK, BASED ON NEUROANATOMICAL AND CLINICAL DATA (2014) (0)
- Factor Analysis on a Graph (2018) (0)
- Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions (2022) (0)
- DME Handout: Support Vector Machines (2002) (0)
- Reduced structural connectivity brain connectivity in amyotrophic lateral sclerosis (2013) (0)
- Neighbourhood tractography: a new approach to seed point placement for fibre tracking (2006) (0)
- The Conference on Uncertainty in Artificial Intelligence (UAI 2017) (2017) (0)
- 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 (2023) (0)
- CLEANING ASTRONOMICAL DATABASES USING HOUGH TRANSFORMS AND RENEWAL STRINGS (2004) (0)
- Structural Brain Networks in Amyotrophic Lateral Sclerosis (2013) (0)
- Edinburgh Research Explorer Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks (2018) (0)
- Substituting Convolutions for Neural Network Compression (2021) (0)
- The test-retest reliability of structural brain networks obtained from diffusion MRI (2013) (0)
- Resource-Efficient Feature Gathering at Test Time (2016) (0)
- Robust segmentation of white matter tracts in the aging brain (2007) (0)
- Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03) (2014) (0)
- Hamiltonian Latent Operators for content and motion disentanglement in image sequences (2021) (0)
- Machine Learning and Pattern Recognition Density Estimation : Gaussians Course Lecturer : (2009) (0)
- Estimating white matter tract volume in partial volume voxels with diffusion MRI (2009) (0)
- Hamiltonian Operator Disentanglement of Content and Motion in Image Sequences (2021) (0)
- BLOCKSWAP: FISHER-GUIDED BLOCK SUBSTITUTION (2019) (0)
- Discovering white matter structure beyond anisotropy maps with diffusion MRI (2010) (0)
- Brain white matter structure and information processing speed in healthy older age (2015) (0)
- A comparison of seeding strategies for group tractography (2007) (0)
- Learning from Data Generalisation (2004) (0)
- Robust and efficient computation of retinal fractal dimension through deep approximation (2022) (0)
- 546-P: Deep Learning Predictions of Diabetic Retinopathy Associated with Progression of Renal Disease in Type 1 Diabetes (2019) (0)
- Probing the Brain's White Matter with Tissue Dependent Diffusion Model (2012) (0)
- Comparison Between FWE and FDR Corrections for Threshold Free Cluster Enhancement Maps (2011) (0)
- Comparison of neighborhood tractography methods for segmenting white matter tracts in the ageing brain (2008) (0)
- A high capacity incremental and local learning algorithm for attractor neural networks (2007) (0)
- Proposal Adaptive Machine Learning to the problem of Automated Data Linkage (2005) (0)
- Dynami Positional Trees for Stru tural Image Analysis (2000) (0)
- Homeostasis causes hallucinations in a hierarchical generative model of the visual cortex: the Charles Bonnet Syndrome (2011) (0)
- Neighbourhood tractography : a new approach to seed point placement for group fibre tracking (2006) (0)
- Unifying low-level mechanistic and high-level Bayesian explanations of bistable perceptions: neuronal adaptation for cortical inference (2011) (0)
- Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry (2022) (0)
- Explorer Towards a Neural Statistician (2017) (0)
- Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols (2022) (0)
- Super-Resolution of Magnetic Resonance Images Acquired Under Clinical Protocols using Deep Attention-based Method (2022) (0)
- LATTER M INIMA WITH SGD (2018) (0)
- ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT I (2011) (0)
- Prediction-Guided Distillation for Dense Object Detection (Supplementary Material) (2022) (0)
- Probabilistic combination of tractography data from multiple seed points for white matter segmentation (2007) (0)
- GINN: Geometric Illustration of Neural Networks (2018) (0)
- Class Imbalance in Few-Shot Learning (2021) (0)
- IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007 (2007) (0)
- Contrastive Meta-Learning for Partially Observable Few-Shot Learning (2023) (0)
- Machine Learning and Pattern Recognition Principal Component Analysis Course (2009) (0)
- Explorer Stochastic Parallel Block Coordinate Descent for Large-scale Saddle Point Problems (2017) (0)
- ACAT: Adversarial Counterfactual Attention for Classification and Detection in Medical Imaging (2023) (0)
- UTSG Conference, January 1996 (1996) (0)
- SGD S MOOTHS THE S HARPEST D IRECTIONS (2018) (0)
- Probabilistic Modelling and Reasoning : Assignment Modelling the skills of Go players Instructor : Dr (2017) (0)
- Adversarial robustness of β-VAE through the lens of local geometry (2022) (0)
- Proceedings of the ISMRM, 14th Scientific Meeting Exhibition, Seattle (2006) (0)
- Iterative Supervised Principal Components (2018) (0)
- Multi-scale segmentation of dual-channel MRI using volume resolution enhancement and tubular structure detection (2008) (0)
- Computer-Supported Mathematical Theory Development’04 (2004) (0)
- Proceedings of the British Chapter of the ISMRM, Guildford, UK (2006) (0)
- Evaluating Grouped Spatial Pack Convolutions on Edge CPUs (2020) (0)
- Learning from Data Layered Neural Networks (2004) (0)
- Explorer Series Expansion Approximations of Brownian Motion for NonLinear Kalman Filtering of Diffusion Processes (2018) (0)
- LEARNING FROM DATA (2019) (0)
- Inference in differentiable generative models (2017) (0)
- IRP : Fourier priors for recovering audio data from damaged signals (0)
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What Schools Are Affiliated With Amos Storkey?
Amos Storkey is affiliated with the following schools:
