Estimating the Delay of the fMRI Response. - C. H. Liao; Keith J. Worsley; Jean-Baptiste Poline; John A. D. Aston; G. H. Duncan; Alan C. Evans (NeuroImage, 2002)

A General Statistical Analysis for fMRI Data. - Keith J. Worsley; C. H. Liao; John A. D. Aston; V. Petre; G. H. Duncan; F. Morales; Alan C. Evans (NeuroImage, 2002)

Dynamic functional changes associated with cognitive skill learning of an adapted version of the Tower of London task. - Miriam H. Beauchamp; Alain Dagher; John A. D. Aston; Julien Doyon (NeuroImage, 2003)

A Linear Wavelet Filter for Parametric Imaging with Dynamic PET. - Federico E. Turkheimer; John A. D. Aston; Richard B. Banati; Cyril Riddell; Vincent J. Cunningham (IEEE Trans. Medical Imaging, 2003)

Wavelet variance components in image space for spatiotemporal neuroimaging data. - John A. D. Aston; Roger N. Gunn; Rainer Hinz; Federico E. Turkheimer (NeuroImage, 2005)

A method for generating reproducible evidence in fMRI studies. - Michelle Liou; Hong-Ren Su; Juin-Der Lee; John A. D. Aston; Arthur C. Tsai; Philip E. Cheng (NeuroImage, 2006)

Multi-resolution Bayesian regression in PET dynamic studies using wavelets. - Federico E. Turkheimer; John A. D. Aston; Marie-Claude Asselin; Rainer Hinz (NeuroImage, 2006)

Reproducibility Analysis of Event-Related fMRI Experiments Using Laguerre Polynomials. - Hong-Ren Su; Michelle Liou; Philip E. Cheng; John A. D. Aston; Shang-Hong Lai (ICONIP (1), 2007)

Waiting time distribution of generalized later patterns. - Donald E. K. Martin; John A. D. Aston (Comput. Stat. Data Anal., 2008)

Dynamic Positron Emission Tomography Data-Driven Analysis Using Sparse Bayesian Learning. - Jyh-Ying Peng; John A. D. Aston; R. N. Gunn; Cheng-Yuan Liou; John Ashburner (IEEE Trans. Medical Imaging, 2008)

Smoothing dynamic positron emission tomography time courses using functional principal components. - Ci-Ren Jiang; John A. D. Aston; Jane-Ling Wang (NeuroImage, 2009)

MR Image Segmentation Using a Power Transformation Approach. - Juin-Der Lee; Hong-Ren Su; Philip E. Cheng; Michelle Liou; John A. D. Aston; Arthur C. Tsai; Cheng-Yu Chen (IEEE Trans. Medical Imaging, 2009)

Linguistic and Human Effects on F₀ in a Tonal Dialect of Qiang. - Jonathan Evans; Man-ni Chu; John A. D. Aston; Chao-yu Su (Phonetica, 2010)

Bayesian model comparison via path-sampling sequential Monte Carlo. - Yan Zhou; Adam M. Johansen; John A. D. Aston (SSP, 2012)

Detecting and estimating changes in dependent functional data. - John A. D. Aston; Claudia Kirch (J. Multivar. Anal., 2012)

A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons. - Dimitrios V. Vavoulis; Volko A. Straub; John A. D. Aston; Jianfeng Feng (PLoS Comput. Biol., 2012)

Implied distributions in multiple change point problems. - John A. D. Aston; Jyh-Ying Peng; Donald E. K. Martin (Stat. Comput., 2012)

Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin. - Adam J. Hall; Michael J. Chappell; John A. D. Aston; Stephen A. Ward (Comput. Methods Programs Biomed., 2013)

Reprint of "Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin". - Adam J. Hall; Michael J. Chappell; John A. D. Aston; Stephen A. Ward (Comput. Methods Programs Biomed., 2014)

Reconstructing the sounds of words from the past. - John S. Coleman; John A. D. Aston; Davide Pigole (ICPhS, 2015)

The Uncertainty of Storm Season Changes: Quantifying the Uncertainty of Autocovariance Changepoints. - Christopher F. H. Nam; John A. D. Aston; Idris A. Eckley; Rebecca Killick (Technometrics, 2015)

Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. - Yu Wang 060; Bin Dai; Gang Hua 001; John A. D. Aston; David P. Wipf (UAI, 2017)

Veiled Attributes of the Variational Autoencoder. - Bin Dai; Yu Wang 060; John A. D. Aston; Gang Hua 001; David P. Wipf (CoRR, 2017)

Gaussian tree constraints applied to acoustic linguistic functional data. - Nathaniel Shiers; John A. D. Aston; Jim Q. Smith; John S. Coleman (J. Multivar. Anal., 2017)

Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. - Bin Dai; Yu Wang 060; John A. D. Aston; Gang Hua 001; David P. Wipf (J. Mach. Learn. Res., 2018)

Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers. - Yu Wang 060; Bin Dai; Gang Hua 001; John A. D. Aston; David P. Wipf (IEEE J. Sel. Top. Signal Process., 2018)

A Variational Model Dedicated to Joint Segmentation, Registration, and Atlas Generation for Shape Analysis. - Noémie Debroux; John A. D. Aston; Fabien Bonardi; Alistair Forbes; Carole Le Guyader; Marina Romanchikova; Carola-Bibiane Schönlieb (SIAM J. Imaging Sci., 2020)

Testing for Geometric Invariance and Equivariance. - Louis G. Christie; John A. D. Aston (CoRR, 2022)

Classification of datasets with imputed missing values: does imputation quality matter? - Tolou Shadbahr; Michael Roberts; Jan Stanczuk; Julian D. Gilbey; Philip Teare; Sören Dittmer; Matthew Thorpe; Ramón Viñas Torné 001; Evis Sala; Pietro Lió; Mishal Patel; AIX-COVNET Collaboration; et al. (CoRR, 2022)

Navigating the challenges in creating complex data systems: a development philosophy. - Sören Dittmer; Michael Roberts; Julian D. Gilbey; Ander Biguri; AIX-COVNET Collaboration; Jacobus Preller; James H. F. Rudd; John A. D. Aston; Carola-Bibiane Schönlieb (CoRR, 2022)

On Krylov Methods for Large Scale CBCT Reconstruction. - Malena Sabate Landman; Ander Biguri; Sepideh Hatamikia; Richard P. Boardman; John A. D. Aston; Carola-Bibiane Schönlieb (CoRR, 2022)

Eigen-Adjusted Functional Principal Component Analysis. - Ci-Ren Jiang; Eardi Lila; John A. D. Aston; Jane-Ling Wang (J. Comput. Graph. Stat., 2022)

Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data. - Daniel Kreuter; Samuel Tull; Julian D. Gilbey; Jacobus Preller; BloodCounts Consortium; John A. D. Aston; James H. F. Rudd; Suthesh Sivapalaratnam; Carola-Bibiane Schönlieb; Nicholas S. Gleadall; Michael Roberts (CoRR, 2023)

Reinterpreting survival analysis in the universal approximator age. - Sören Dittmer; Michael Roberts; Jacobus Preller; AIX-COVNET Collaboration; James H. F. Rudd; John A. D. Aston; Carola-Bibiane Schönlieb (CoRR, 2023)

Recent Methodological Advances in Federated Learning for Healthcare. - Fan Zhang; Daniel Kreuter; Yichen Chen; Sören Dittmer; Samuel Tull; Tolou Shadbahr; BloodCounts Collaboration; Jacobus Preller; James H. F. Rudd; John A. D. Aston; Carola-Bibiane Schönlieb; Nicholas S. Gleadall; et al. (CoRR, 2023)

Navigating the development challenges in creating complex data systems. - Sören Dittmer; Michael Roberts; Julian D. Gilbey; Ander Biguri; Ian Selby; Anna Breger; Matthew Thorpe; Jonathan R. Weir-McCall; Effrossyni Gkrania-Klotsas; Anna Korhonen; Emily R. Jefferson; Georg Langs; et al. (Nat. Mac. Intell., 2023)

Recent methodological advances in federated learning for healthcare. - Fan Zhang; Daniel Kreuter; Yichen Chen; Sören Dittmer; Samuel Tull; Tolou Shadbahr; Martijn Schut; Folkert W. Asselbergs; Sujoy Kar; Suthesh Sivapalaratnam; Sophie Williams; Mickey Koh; et al. (Patterns, 2024)

SurvSurf: a partially monotonic neural network for first-hitting time prediction of intermittently observed discrete and continuous sequential events. - Yichen Kelly Chen; Sören Dittmer; Kinga Bernatowicz; Josep Arús-Pous; Kamen Bliznashki; John A. D. Aston; James H. F. Rudd; Carola-Bibiane Schönlieb; James Jones; Michael Roberts (CoRR, 2025)