John M. Winn

According to our database, John M. Winn authored at least 47 papers between 2000 and 2021.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2021

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, Yordan Zaykov

Proceedings of the 3rd Conference on Automated Knowledge Base Construction, 2021

2020

Learning Direct Optimization for scene understanding.

Lukasz Romaszko, Christopher K. I. Williams, John M. Winn

Pattern Recognit., 2020

2019

Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program.

John M. Winn, John Guiver, Sam Webster, Yordan Zaykov, Martin Kukla, Dany Fabian

Proceedings of the 1st Conference on Automated Knowledge Base Construction, 2019

2016

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering.

Liwen Zhang, John M. Winn, Ryota Tomioka

CoRR, 2016

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

Consensus Message Passing for Layered Graphical Models.

Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

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

Just-In-Time Learning for Fast and Flexible Inference.

S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Decision Jungles: Compact and Rich Models for Classification.

Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John M. Winn, Antonio Criminisi

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Learning to Pass Expectation Propagation Messages.

Nicolas Heess, Daniel Tarlow, John M. Winn

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables.

Nevena Lazic, Christopher M. Bishop, John M. Winn

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

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

Causality with Gates.

John M. Winn

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 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

The Shape Boltzmann Machine: A strong model of object shape.

S. M. Ali Eslami, Nicolas Heess, John M. Winn

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

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

Learning a Generative Model of Images by Factoring Appearance and Shape.

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn

Neural Comput., 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

Proceedings of the Information Processing in Medical Imaging, 2011

Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs.

Nicolas Heess, Nicolas Le Roux, John M. Winn

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

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

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

2009

Epitomic Location Recognition.

Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn

IEEE Trans. Pattern Anal. Mach. Intell., 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

A unified modeling approach to data-intensive healthcare.

Iain E. Buchan, John M. Winn, Christopher M. Bishop

Proceedings of the Fourth Paradigm: Data-Intensive Scientific Discovery, 2009

2008

Accounting for Non-genetic Factors Improves the Power of eQTL Studies.

Oliver Stegle, Anitha Kannan, Richard Durbin, John M. Winn

Proceedings of the Research in Computational Molecular Biology, 2008

Gates.

Tom Minka, John M. Winn

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Immune System Modeling with Infer.NET.

Vincent Y. F. Tan, John M. Winn, Angela Simpson, Adnan Custovic

Proceedings of the Fourth International Conference on e-Science, 2008

2007

Photo clip art.

Jean-François Lalonde, Derek Hoiem, Alexei A. Efros, Carsten Rother, John M. Winn, Antonio Criminisi

ACM Trans. Graph., 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, Andrew W. Fitzgibbon

Proceedings of the Second IEEE International Workshop on Horizontal Interactive Human-Computer Systems (Tabletop 2007), 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

Proceedings of the Proceedings 15th International Conference on Intelligent Systems for Molecular Biology (ISMB) & 6th European Conference on Computational Biology (ECCB), 2007

Tree-based Classifiers for Bilayer Video Segmentation.

Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa

Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 2007

Hybrid learning of large jigsaws.

Julia A. Lasserre, Anitha Kannan, John M. Winn

Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 2007

3D LayoutCRF for Multi-View Object Class Recognition and Segmentation.

Derek Hoiem, Carsten Rother, John M. Winn

Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 2007

Incorporating On-demand Stereo for Real Time Recognition.

Thomas Deselaers, Antonio Criminisi, John M. Winn, Ankur Agarwal

Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 2007

2006

Clustering appearance and shape by learning jigsaws.

Anitha Kannan, John M. Winn, Carsten Rother

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

<i>TextonBoost</i>: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation.

Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi

Proceedings of the Computer Vision, 2006

Located Hidden Random Fields: Learning Discriminative Parts for Object Detection.

Ashish Kapoor, John M. Winn

Proceedings of the Computer Vision, 2006

The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects.

John M. Winn, Jamie Shotton

Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

Discriminative Object Class Models of Appearance and Shape by Correlatons.

Silvio Savarese, John M. Winn, Antonio Criminisi

Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video.

Nebojsa Jojic, John M. Winn, C. Lawrence Zitnick

Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

2005

Variational Message Passing.

John M. Winn, Christopher M. Bishop

J. Mach. Learn. Res., 2005

LOCUS: Learning Object Classes with Unsupervised Segmentation.

John M. Winn, Nebojsa Jojic

Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV 2005), 2005

Object Categorization by Learned Universal Visual Dictionary.

John M. Winn, Antonio Criminisi, Thomas P. Minka

Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV 2005), 2005

2004

Generative Affine Localisation and Tracking.

John M. Winn, Andrew Blake

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

Structured Variational Distributions in VIBES.

Christopher M. Bishop, John M. Winn

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002

VIBES: A Variational Inference Engine for Bayesian Networks.

Christopher M. Bishop, David J. Spiegelhalter, John M. Winn

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2000

Non-linear Bayesian Image Modelling.

Christopher M. Bishop, John M. Winn

Proceedings of the Computer Vision - ECCV 2000, 6th European Conference on Computer Vision, Dublin, Ireland, June 26, 2000