J. Winn

发表

Located Hidden Random Fields: Learning Discriminative Parts for Object Detection

Ashish Kapoor, John M. Winn, Ashish Kapoor, 2006, ECCV.

Variational Message Passing

Charles M. Bishop, J. Winn, 2005, J. Mach. Learn. Res..

The Pascal Visual Object Classes (VOC) Challenge

Luc Van Gool, Andrew Zisserman, Christopher K. I. Williams, 2010, International Journal of Computer Vision.

The Pascal Visual Object Classes Challenge: A Retrospective

Luc Van Gool, Andrew Zisserman, Christopher K. I. Williams, 2014, International Journal of Computer Vision.

Structured Variational Distributions in VIBES

Christopher M. Bishop, John M. Winn, Charles M. Bishop, 2003, AISTATS.

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

Nicolas Le Roux, Nicolas Heess, John M. Winn, 2011, ICANN.

TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation

Antonio Criminisi, Carsten Rother, Jamie Shotton, 2006, ECCV.

The Learning Workshop Snowbird

Nicolas Le Roux, N. Heess, J. Winn, 2010 .

Learning to Pass Expectation Propagation Messages

Daniel Tarlow, Nicolas Heess, John M. Winn, 2013, NIPS.

Learning a Generative Model of Images by Factoring Appearance and Shape

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, 2011, Neural Computation.

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

Derek Hoiem, Carsten Rother, John M. Winn, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

Jamie Shotton, John M. Winn, J. Winn, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

Object categorization by learned universal visual dictionary

Antonio Criminisi, Tom Minka, John M. Winn, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

LOCUS: learning object classes with unsupervised segmentation

Nebojsa Jojic, John M. Winn, J. Winn, 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

Variational Message Passing and its Applications

John M. Winn, J. Winn, 2004 .

Photo clip art

Alexei A. Efros, Antonio Criminisi, Derek Hoiem, 2007, ACM Trans. Graph..

VIBES: A Variational Inference Engine for Bayesian Networks

David J. Spiegelhalter, Christopher M. Bishop, John M. Winn, 2002, NIPS.

Assessing the significance of performance differences on the PASCAL VOC challenges via bootstrapping

Luc Van Gool, Andrew Zisserman, Christopher K. I. Williams, 2013 .

Entanglement and Differentiable Information Gain Maximization

Dimitris N. Metaxas, J. E. Iglesias, J. Winn, 2013 .

The Shape Boltzmann Machine: A strong model of object shape

Nicolas Heess, John M. Winn, S. M. Ali Eslami, 2012, CVPR.

Tree-based Classifiers for Bilayer Video Segmentation

Irfan A. Essa, Antonio Criminisi, Pei Yin, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

Clustering appearance and shape by learning jigsaws

Carsten Rother, John M. Winn, Anitha Kannan, 2006, NIPS.

Object Class Recognition at a Glance

Antonio Criminisi, John Winn, J. Winn, 2006 .

Decision Jungles: Compact and Rich Models for Classification

Sebastian Nowozin, Toby Sharp, Antonio Criminisi, 2013, NIPS.

Epitomic Location Recognition

Antonio Criminisi, Kai Ni, John M. Winn, 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

Oliver Stegle, John M. Winn, Richard Durbin, 2008, RECOMB.

Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images

Dimitris N. Metaxas, Antonio Criminisi, Albert Montillo, 2011, IPMI.

Just-In-Time Learning for Fast and Flexible Inference

Pushmeet Kohli, Daniel Tarlow, John M. Winn, 2014, NIPS.

Consensus Message Passing for Layered Graphical Models pdf

Varun Jampani, Pushmeet Kohli, Daniel Tarlow, 2014, AISTATS.

A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies

Leopold Parts, Oliver Stegle, John M. Winn, 2010, PLoS Comput. Biol..

Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers

Irfan A. Essa, Antonio Criminisi, Pei Yin, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

ShapePheno: unsupervised extraction of shape phenotypes from biological image collections

Daniel P. Miranker, Oliver Stegle, Karsten M. Borgwardt, 2012, Bioinform..

Immune System Modeling with Infer.NET

Vincent Y. F. Tan, John M. Winn, Adnan Custovic, 2008, 2008 IEEE Fourth International Conference on eScience.

A unified modeling approach to data-intensive healthcare

Iain E. Buchan, Christopher M. Bishop, John M. Winn, 2009, The Fourth Paradigm.

Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes

R. Durbin, J. Winn, O. Stegle, 2011, PLoS genetics.

Extent, Causes, and Consequences of Small RNA Expression Variation in Human Adipose Tissue

Anton J. Enright, T. Spector, R. Mägi, 2012, PLoS genetics.

Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

R. Durbin, J. Winn, O. Stegle, 2012, Nature Protocols.

Gates Tom Minka

T. Minka, J. Winn, 2008 .

Explorer Variational Message Passing

J. Winn, 2005 .

Gates

Thomas P. Minka, John Winn, T. Minka, 2008, NIPS.

Pathway-Based Factor Analysis of Gene Expression Data Produces Highly Heritable Phenotypes That Associate with Age

T. Spector, R. Durbin, J. Winn, 2015, G3: Genes, Genomes, Genetics.

Advances in Neural Information Processing Systems 15 (NIPS 2002)

David J. Spiegelhalter, Christopher M. Bishop, John Winn, 2002 .

The Fourth Paradigm: Data-Intensive Scientific Discovery

Christopher M. Bishop, Iain Buchan, John Winn, 2009 .

Photo clip art

Alexei A. Efros, C. Rother, J. Winn, 2007, ACM Trans. Graph..

C-Slate: Exploring Remote Collaboration on Horizontal Multi-touch Surfaces

Antonio Criminisi, Shahram Izadi, Andrew Blake, 2007 .

A Strong Model of Object Shape

Christopher K. I. Williams, N. Heess, J. Winn, 2017 .

Joint learning of latent variables and structure in Bayesian networks

J. Winn, Nevena Lazic, 2022 .

Edinburgh Research Explorer The PASCAL Visual Object Classes (VOC) Challenge

L. Gool, J. Winn, M. Everingham, 2009 .

Beyond atopy: multiple patterns of sensitization in relation to asthma in a birth cohort study.

Markus Svensén, Iain Buchan, Adnan Custovic, 2010, American journal of respiratory and critical care medicine.

TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

Antonio Criminisi, Carsten Rother, Jamie Shotton, 2007, International Journal of Computer Vision.

Discriminative Object Class Models of Appearance and Shape by Correlatons

Silvio Savarese, Antonio Criminisi, John M. Winn, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

Pathway-Based Factor Analysis of Gene Expression Data Produces Highly Heritable Phenotypes That Associate with Age

T. Spector, R. Durbin, J. Winn, 2015, G3: Genes, Genomes, Genetics.

Non-linear Bayesian Image Modelling

Christopher M. Bishop, John M. Winn, Charles M. Bishop, 2000, ECCV.

The Shape Boltzmann Machine: A Strong Model of Object Shape

Christopher K. I. Williams, Nicolas Heess, John M. Winn, 2012, International Journal of Computer Vision.

Causality with Gates

John M. Winn, J. Winn, 2012, AISTATS.

Healthcare e-Labs: Opening and Integrating Models of Health

J. Winn, D. Hoyle, J. Ainsworth, 2009 .

Gates: A Graphical Notation for Mixture Models

Thomas P. Minka, John Winn, T. Minka, 2008 .

A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD

T. Spector, J. Winn, S. Chiappa, 2013, Respiratory Research.

Probabilistic Programming with Infer.NET

Thomas P. Minka, John Winn, T. Minka, 2017 .

International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge

Christopher K. I. Williams, L. Gool, J. Winn, 2022 .

The Pascal Visual Object Classes (VOC) Challenge

Christopher K. I. Williams, Luc Van Gool, L. Gool, 2010, International Journal of Computer Vision.

Multiple atopy phenotypes and their associations with asthma: similar findings from two birth cohorts

J. Winn, A. Custovic, G. Roberts, 2013, Allergy.

C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces

A. Agarwal, A. Criminisi, A. Blake, 2007, Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer Systems (TABLETOP'07).

Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities

Siân E. Lindley, Denise J. Wilkins, J. Guiver, 2021, AKBC.

VIBES: A Variational Inference Engine for Bayesian Networks

Charles M. Bishop, D. Spiegelhalter, J. Winn, 2002, NIPS.

Inference algorithms and learning theory for Bayesian sparse factor analysis

Magnus Rattray, Oliver Stegle, John Winn, 2009 .

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

Nebojsa Jojic, C. Lawrence Zitnick, John M. Winn, 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

Generative Affine Localisation and Tracking

Andrew Blake, John M. Winn, J. Winn, 2004, NIPS.

Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers

J. Winn, A. Criminisi, Irfan Essa, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

Yordan Zaykov, John Guiver, John Winn, 2019, AKBC.

TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation

C. Rother, J. Winn, J. Shotton, 2006, ECCV.

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

Ryota Tomioka, John M. Winn, Liwen Zhang, 2016, ArXiv.