John Winn's Publications
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- S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, and John Winn, The Shape Boltzmann Machine: A Strong Model of Object Shape, in International Journal of Computer Vision, Springer, November 2013.
- Silvia Chiappa, John Winn, Ana Viñuela, Hannah Tipney, and Timothy D. Spector, A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD, in Respiratory Research, vol. 14:60, 30 May 2013.
- Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John Winn, and Karsten M. Borgwardt, ShapePheno: Unsupervised extraction of shape phenotypes from biological image collections, in Bioinformatics, pp. 1001-1008, Oxford University Press, February 2012.
- Oliver Stegle, Leopold Parts, Matias Piipari, John Winn, and Richard Durbin, Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses, in Nature Protocols, vol. 7, pp. 500-507, Nature Publishing Group, February 2012.
- L. Parts, Å.K. Hedman, S. Keildson, A.J. Knights, C. Abreu-Goodger, M. van de Bunt, J.A. Guerra-Assunção, N. Bartonicek, S. van Dongen, R. Mägi, J. Nesbit, A. Barrett, M. Rantalainen, A. C. Nica, M. A. Quail, K. S. Small, D. Glass, A. J. Enright, J. Winn, P. Deloukas, E. T. Dermitzakis, M. I. McCarthy, T. D. Spector, R. Durbin, and C. M. Lindgren, Extent, Causes, and Consequences of Small RNA Expression Variation in Human Adipose Tissue, in PLoS Genetics, vol. 8, no. 5, pp. e1002704, Public Library of Science, 2012.
- Nicolas Le Roux, Nicolas Heess, Jamie Shotton, and John Winn, Learning a Generative Model of Images by Factoring Appearance and Shape , in Neural Computation, vol. 23, no. 3, pp. 593-650, MIT Press, March 2011.
- Leopold Parts, Oliver Stegle, John Winn, and Richard Durbin, Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes, in PLoS Genetics, PLoS, January 2011.
- Oliver Stegle, Leopold Parts, Richard Durbin, and John Winn, A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies, in PLoS Computational Biology, PLoS Computational Biology (Public Library of Science Computational Biology), , 6 May 2010.
- A. Simpson, V.Y. Tan, J. Winn, M. Svensen, C.M. Bishop, D.E. Heckerman, I. Buchan, and A. Custovic, Beyond Atopy: Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study, in Am J Respir Crit Care Med, 18 February 2010.
- Pei Yin, Antonio Criminisi, John Winn, and Irfan Essa, Bilayer Segmentation of Webcam Videos Using Tree-based Classifiers, in Trans. Pattern Analysis and Machine Intelligence (PAMI), IEEE, 2010.
- D. Knowles, L. Parts, D. Glass, and J. Winn, Modeling skin and ageing phenotypes using latent variable models in Infer.NET, in Predictive Models in Personalized Medicine Workshop, NIPS, , 2010.
- Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman, The Pascal Visual Object Classes (VOC) Challenge, in International Journal of Computer Vision, vol. 88, no. 2, pp. 303-308, Springer Verlag, 9 September 2009.
- Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI special issue), IEEE, 2009.
- Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi, TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context, in Int. Journal of Computer Vision (IJCV), Springer Verlag, January 2009.
- John Winn and Christopher M. Bishop, Variational Message Passing, in Journal of Machine Learning Research, vol. 5, January 2004.
- Varun Jampani, SM Ali Eslami, Daniel Tarlow, Pushmeet Kohli, and John Winn, Consensus Message Passing for Layered Graphical Models, in The 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015), , May 2015.
- S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, and John Winn, Just-In-Time Learning for Fast and Flexible Inference, December 2014.
- Nevena Lazic, C. M. Bishop, and J. Winn, Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables, in Proceedings Sixteenth International Conference on Artificial Intelligence and Statistics (AIStats), AISTATS, 2013.
- Nicolas Heess, Daniel Tarlow, and John Winn, Learning to Pass Expectation Propagation Messages, in Advances in Neural Information Processing Systems, 2013.
- Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, and Antonio Criminisi, Decision Jungles: Compact and Rich Models for Classification, in Proc. NIPS, 2013.
- S. M. Eslami, N. Heess, and J. Winn, The Shape Boltzmann Machine: a Strong Model of Object Shape, in Proc. Conf. Computer Vision and Pattern Recognition (to appear), July 2012.
- John Winn, Causality with Gates, in Proceedings Artificial Intelligence and Statistics, The Society for Artificial Intelligence and Statistics, April 2012.
- A. Montillo, J. Shotton, J. Winn, J. E. Iglesias, D. Metaxas, and A. Criminisi, Entangled Decision Forests and their Application for Semantic Segmentation of CT Images, in Information Processing in Medical Imaging (IPMI), July 2011.
- Nicolas Heess, Nicolas Le Roux, and John Winn, Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs, in International Conference on Artificial Neural Networks, 2011.
- A. Criminisi, A. Montillo, J. Shotton, J. Winn, S. Pathak, and K. Siddiqui, Automatic Semantic Segmentation of Anatomical Structures in CT Scans, in Radiological Society of North America (RSNA), 2010.
- Tom Minka and John Winn, Gates, in Advances in Neural Information Processing Systems 21, 2009.
- Magnus Rattray, Oliver Stegle, Kevin Sharp, and John Winn, Inference algorithms and learning theory for Bayesian sparse factor analysis, in International Workshop on Statistical-Mechanical Informatics 2009, Journal of Physics: Conference Series, 2009.
- Vincent Y. F. Tan, John Winn, Angela Simpson, and Adnan Custovic, Immune System Modeling with Infer.NET, in IEEE International Conference on e-Science (e-Science 2008), , Indianapolis, Indiana, 1 December 2008.
- Oliver Stegle, Anitha Kannan, Richard Durbin, and John M. Winn, Accounting for Non-genetic Factors Improves the Power of eQTL Studies, in International Conference on Research in Computational Molecular Biology, 2008.
- Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in Proc IEEE Conference on Computer Vision (CVPR). Winner of BEST STUDENT PAPER RUNNER UP AWARD., IEEE Computer Society, 2008.
- D. Hoeim, C. Rother, and J. Winn, 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation , in Proc. IEEE Computer Vision and Pattern Recognition (CVPR) , Minneapolis, US, 2007.
- Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in Advances in Neural Information Processing Systems, MIT Press, 2007.
- Jean Francois Lalonde, Derek Hoiem, Alyosha A Efros, John Winn, Carsten Rother, and Antonio Criminisi, Photo Clip Art, in Proc. ACM SIGGRAPH, 2007.
- Pei Yin, Antonio Criminisi, Irfan Essa, and John Winn, Tree-based Classifiers for Bilayer Video Segmentation, in Proc. Conf. Computer Vision and Pattern Recognition, 2007.
- Jim C. Huang, Anitha Kannan, and John M. Winn, Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations, in International Conference on Intelligent Systems for Molecular Biology, 2007.
- Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John Winn, Andrew Blake, and Andrew Fitzgibbon, C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces, in Proceedings of the Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer Systems (Tabletop 2007), IEEE, 2007.
- Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John Winn, Andrew Blake, and Andrew Fitzgibbon, C-Slate: Exploring Remote Collaboration on Horizontal Multi-touch Surfaces. , in Proc. IEEE Tabletop, 2007.
- Julia Lasserre, Anitha Kannan, and John Winn, Hybrid learning of large jigsaws, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
- Thomas Deselaers, Antonio Criminisi, John Winn, and Ankur Agarwal, Incorporating On-demand Stereo for Real Time Recognition, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2007.
- John Winn and Antonio Criminisi, Object Class Recognition at a Glance, in Proc. Conf. Computer Vision and Pattern Recognition (CVPR) -- Video Track, 2006.
- A. Montillo, J. Tu, J. Shotton, J. Winn, J.E. Iglesias, D.N. Metaxas, and A. Criminisi, Entanglement and Differentiable Information Gain Maximization, in Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013.
- John Winn and Jamie Shotton, Markov Random Fields for Object Detection, in Markov Random Fields for Vision and Image Processing, pp. 389-404, MIT Press, 2011.
- Nicolas Le Roux, Nicolas Heess, Jamie Shotton, and John Winn, Learning a generative model of images by factoring appearance and shape, no. MSR-TR-2010-7, January 2010.
- Iain Buchan, John Winn, and Christopher Bishop, A Unified Modeling Approach to Data-Intensive Healthcare, in The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, 2009.
- Tom Minka and John Winn, Gates: A graphical notation for mixture models, no. MSR-TR-2008-185, 5 December 2008.