Non-linear Bayesian Image Modelling. - Christopher M. Bishop; John M. Winn (ECCV (1), 2000)

VIBES: A Variational Inference Engine for Bayesian Networks. - Christopher M. Bishop; David J. Spiegelhalter; John M. Winn (NIPS, 2002)

Structured Variational Distributions in VIBES. - Christopher M. Bishop; John M. Winn (AISTATS, 2003)

Generative Affine Localisation and Tracking. - John M. Winn; Andrew Blake 001 (NIPS, 2004)

Object Categorization by Learned Universal Visual Dictionary. - John M. Winn; Antonio Criminisi; Thomas P. Minka (ICCV, 2005)

LOCUS: Learning Object Classes with Unsupervised Segmentation. - John M. Winn; Nebojsa Jojic (ICCV, 2005)

Variational Message Passing. - John M. Winn; Christopher M. Bishop (J. Mach. Learn. Res., 2005)

Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video. - Nebojsa Jojic; John M. Winn; C. Lawrence Zitnick (CVPR (1), 2006)

Discriminative Object Class Models of Appearance and Shape by Correlatons. - Silvio Savarese; John M. Winn; Antonio Criminisi (CVPR (2), 2006)

The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects. - John M. Winn; Jamie Shotton (CVPR (1), 2006)

Located Hidden Random Fields: Learning Discriminative Parts for Object Detection. - Ashish Kapoor; John M. Winn (ECCV (3), 2006)

TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation. - Jamie Shotton; John M. Winn; Carsten Rother; Antonio Criminisi (ECCV (1), 2006)

Clustering appearance and shape by learning jigsaws. - Anitha Kannan; John M. Winn; Carsten Rother (NIPS, 2006)

Incorporating On-demand Stereo for Real Time Recognition. - Thomas Deselaers; Antonio Criminisi; John M. Winn; Ankur Agarwal (CVPR, 2007)

3D LayoutCRF for Multi-View Object Class Recognition and Segmentation. - Derek Hoiem; Carsten Rother; John M. Winn (CVPR, 2007)

Hybrid learning of large jigsaws. - Julia A. Lasserre; Anitha Kannan; John M. Winn (CVPR, 2007)

Tree-based Classifiers for Bilayer Video Segmentation. - Pei Yin; Antonio Criminisi; John M. Winn; Irfan A. Essa (CVPR, 2007)

Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations. - Jim C. Huang; Anitha Kannan; John M. Winn (ISMB/ECCB (Supplement of Bioinformatics), 2007)

C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces. - Shahram Izadi; Ankur Agarwal; Antonio Criminisi; John M. Winn; Andrew Blake 001; Andrew W. Fitzgibbon (Tabletop, 2007)

Photo clip art. - Jean-François Lalonde; Derek Hoiem; Alexei A. Efros; Carsten Rother; John M. Winn; Antonio Criminisi (ACM Trans. Graph., 2007)

Immune System Modeling with Infer.NET. - Vincent Y. F. Tan; John M. Winn; Angela Simpson; Adnan Custovic (eScience, 2008)

Gates. - Tom Minka; John M. Winn (NIPS, 2008)

Accounting for Non-genetic Factors Improves the Power of eQTL Studies. - Oliver Stegle; Anitha Kannan; Richard Durbin; John M. Winn (RECOMB, 2008)

A unified modeling approach to data-intensive healthcare. - Iain E. Buchan; John M. Winn; Christopher M. Bishop (The Fourth Paradigm, 2009)

TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. - Jamie Shotton; John M. Winn; Carsten Rother; Antonio Criminisi (Int. J. Comput. Vis., 2009)

Epitomic Location Recognition. - Kai Ni; Anitha Kannan; Antonio Criminisi; John M. Winn (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

The Pascal Visual Object Classes (VOC) Challenge. - Mark Everingham; Luc Van Gool; Christopher K. I. Williams; John M. Winn; Andrew Zisserman (Int. J. Comput. Vis., 2010)

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies. - Oliver Stegle; Leopold Parts; Richard Durbin; John M. Winn (PLoS Comput. Biol., 2010)

Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. - Nicolas Heess; Nicolas Le Roux; John M. Winn (ICANN (2), 2011)

Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. - Albert Montillo; Jamie Shotton; John M. Winn; Juan Eugenio Iglesias; Dimitris N. Metaxas; Antonio Criminisi (IPMI, 2011)

Learning a Generative Model of Images by Factoring Appearance and Shape. - Nicolas Le Roux; Nicolas Heess; Jamie Shotton; John M. Winn (Neural Comput., 2011)

Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers. - Pei Yin; Antonio Criminisi; John M. Winn; Irfan A. Essa (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

The Shape Boltzmann Machine: A strong model of object shape. - S. M. Ali Eslami; Nicolas Heess; John M. Winn (CVPR, 2012)

ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. - Theofanis Karaletsos; Oliver Stegle; Christine Dreyer; John M. Winn; Karsten M. Borgwardt (Bioinform., 2012)

Causality with Gates. - John M. Winn (AISTATS, 2012)

In Memoriam: Mark Everingham. - Andrew Zisserman; John M. Winn; Andrew W. Fitzgibbon; Luc Van Gool; Josef Sivic; Christopher K. I. Williams; David C. Hogg (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables. - Nevena Lazic; Christopher M. Bishop; John M. Winn (AISTATS, 2013)

Learning to Pass Expectation Propagation Messages. - Nicolas Heess; Daniel Tarlow; John M. Winn (NIPS, 2013)

Decision Jungles: Compact and Rich Models for Classification. - Jamie Shotton; Toby Sharp; Pushmeet Kohli; Sebastian Nowozin; John M. Winn; Antonio Criminisi (NIPS, 2013)

Just-In-Time Learning for Fast and Flexible Inference. - S. M. Ali Eslami; Daniel Tarlow; Pushmeet Kohli; John M. Winn (NIPS, 2014)

The Shape Boltzmann Machine: A Strong Model of Object Shape. - S. M. Ali Eslami; Nicolas Heess; Christopher K. I. Williams; John M. Winn (Int. J. Comput. Vis., 2014)

Consensus Message Passing for Layered Graphical Models. - Varun Jampani; S. M. Ali Eslami; Daniel Tarlow; Pushmeet Kohli; John M. Winn (AISTATS, 2015)

The Pascal Visual Object Classes Challenge: A Retrospective. - Mark Everingham; S. M. Ali Eslami; Luc Van Gool; Christopher K. I. Williams; John M. Winn; Andrew Zisserman (Int. J. Comput. Vis., 2015)

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. - Liwen Zhang; John M. Winn; Ryota Tomioka (CoRR, 2016)

Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program. - John M. Winn; John Guiver; Sam Webster; Yordan Zaykov; Martin Kukla; Dany Fabian (AKBC, 2019)

Learning Direct Optimization for scene understanding. - Lukasz Romaszko; Christopher K. I. Williams; John M. Winn (Pattern Recognit., 2020)

Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities. - John M. Winn; Matteo Venanzi; Tom Minka; Ivan Korostelev; John Guiver; Elena Pochernina; Pavel Mishkov; Alex Spengler; Denise J. Wilkins; Siân E. Lindley; Richard Banks; Sam Webster; et al. (AKBC, 2021)