John M. Winn - RankMe

50 papers A* 8A 4B 1Journal 17Unranked 19

Year	Rank	Type	Title / Venue / Authors
2021	—	`conf`	Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities. AKBC 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
2020	J	`jnl`	Learning Direct Optimization for scene understanding. Pattern Recognit. Lukasz Romaszko, Christopher K. I. Williams, John M. Winn
2019	—	`conf`	Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program. AKBC John M. Winn, John Guiver, Sam Webster, Yordan Zaykov, Martin Kukla, Dany Fabian
2018	J	`jnl`	Learning Direct Optimization for Scene Understanding. CoRR Lukasz Romaszko, Christopher K. I. Williams, John M. Winn
2016	J	`jnl`	Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering. CoRR Liwen Zhang, John M. Winn, Ryota Tomioka
2015	A	`conf`	Consensus Message Passing for Layered Graphical Models. AISTATS Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
2015	J	`jnl`	The Pascal Visual Object Classes Challenge: A Retrospective. Int. J. Comput. Vis. Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman
2014	J	`jnl`	Consensus Message Passing for Layered Graphical Models. CoRR Varun Jampani, S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
2014	—	`conf`	Just-In-Time Learning for Fast and Flexible Inference. NIPS S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John M. Winn
2014	J	`jnl`	The Shape Boltzmann Machine: A Strong Model of Object Shape. Int. J. Comput. Vis. S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, John M. Winn
2013	—	`conf`	Decision Jungles: Compact and Rich Models for Classification. NIPS Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John M. Winn, Antonio Criminisi
2013	—	`conf`	Learning to Pass Expectation Propagation Messages. NIPS Nicolas Heess, Daniel Tarlow, John M. Winn
2013	A	`conf`	Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables. AISTATS Nevena Lazic, Christopher M. Bishop, John M. Winn
2012	A	`conf`	Causality with Gates. AISTATS John M. Winn
2012	J	`jnl`	In Memoriam: Mark Everingham. IEEE Trans. Pattern Anal. Mach. Intell. Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc Van Gool, Josef Sivic, Christopher K. I. Williams, David C. Hogg
2012	J	`jnl`	ShapePheno: unsupervised extraction of shape phenotypes from biological image collections. Bioinform. Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John M. Winn, Karsten M. Borgwardt
2012	A*	`conf`	The Shape Boltzmann Machine: A strong model of object shape. CVPR S. M. Ali Eslami, Nicolas Heess, John M. Winn
2011	J	`jnl`	Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa
2011	—	`conf`	Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images. IPMI Albert Montillo, Jamie Shotton, John M. Winn, Juan Eugenio Iglesias, Dimitris N. Metaxas, Antonio Criminisi
2011	J	`jnl`	Learning a Generative Model of Images by Factoring Appearance and Shape. Neural Comput. Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John M. Winn
2011	—	`conf`	Weakly Supervised Learning of Foreground-Background Segmentation Using Masked RBMs. ICANN (2) Nicolas Heess, Nicolas Le Roux, John M. Winn
2011	J	`jnl`	Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs CoRR Nicolas Heess, Nicolas Le Roux, John M. Winn
2010	J	`jnl`	A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies. PLoS Comput. Biol. Oliver Stegle, Leopold Parts, Richard Durbin, John M. Winn
2010	J	`jnl`	The Pascal Visual Object Classes (VOC) Challenge. Int. J. Comput. Vis. Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman
2009	—	`ch.`	A unified modeling approach to data-intensive healthcare. The Fourth Paradigm Iain E. Buchan, John M. Winn, Christopher M. Bishop
2009	J	`jnl`	Epitomic Location Recognition. IEEE Trans. Pattern Anal. Mach. Intell. Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn
2009	J	`jnl`	TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context. Int. J. Comput. Vis. Jamie Shotton, John M. Winn, Carsten Rother, Antonio Criminisi
2008	B	`conf`	Accounting for Non-genetic Factors Improves the Power of eQTL Studies. RECOMB Oliver Stegle, Anitha Kannan, Richard Durbin, John M. Winn
2008	A*	`conf`	Epitomic location recognition. CVPR Kai Ni, Anitha Kannan, Antonio Criminisi, John M. Winn
2008	—	`conf`	Gates. NIPS Tom Minka, John M. Winn
2008	—	`conf`	Immune System Modeling with Infer.NET. eScience Vincent Y. F. Tan, John M. Winn, Angela Simpson, Adnan Custovic
2007	A*	`conf`	3D LayoutCRF for Multi-View Object Class Recognition and Segmentation. CVPR Derek Hoiem, Carsten Rother, John M. Winn
2007	—	`conf`	Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations. ISMB/ECCB (Supplement of Bioinformatics) Jim C. Huang, Anitha Kannan, John M. Winn
2007	—	`conf`	C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces. Tabletop Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John M. Winn, Andrew Blake, Andrew W. Fitzgibbon
2007	A*	`conf`	Hybrid learning of large jigsaws. CVPR Julia A. Lasserre, Anitha Kannan, John M. Winn
2007	A*	`conf`	Incorporating On-demand Stereo for Real Time Recognition. CVPR Thomas Deselaers, Antonio Criminisi, John M. Winn, Ankur Agarwal
2007	J	`jnl`	Photo clip art. ACM Trans. Graph. Jean-François Lalonde, Derek Hoiem, Alexei A. Efros, Carsten Rother, John M. Winn, Antonio Criminisi
2007	A*	`conf`	Tree-based Classifiers for Bilayer Video Segmentation. CVPR Pei Yin, Antonio Criminisi, John M. Winn, Irfan A. Essa
2006	—	`conf`	Clustering appearance and shape by learning jigsaws. NIPS Anitha Kannan, John M. Winn, Carsten Rother
2006	—	`conf`	Discriminative Object Class Models of Appearance and Shape by Correlatons. CVPR (2) Silvio Savarese, John M. Winn, Antonio Criminisi
2006	—	`conf`	Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video. CVPR (1) Nebojsa Jojic, John M. Winn, C. Lawrence Zitnick
2006	—	`conf`	Located Hidden Random Fields: Learning Discriminative Parts for Object Detection. ECCV (3) Ashish Kapoor, John M. Winn
2006	—	`conf`	The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects. CVPR (1) John M. Winn, Jamie Shotton
2005	A*	`conf`	LOCUS: Learning Object Classes with Unsupervised Segmentation. ICCV John M. Winn, Nebojsa Jojic
2005	A*	`conf`	Object Categorization by Learned Universal Visual Dictionary. ICCV John M. Winn, Antonio Criminisi, Thomas P. Minka
2005	J	`jnl`	Variational Message Passing. J. Mach. Learn. Res. John M. Winn, Christopher M. Bishop
2004	—	`conf`	Generative Affine Localisation and Tracking. NIPS John M. Winn, Andrew Blake
2003	A	`conf`	Structured Variational Distributions in VIBES. AISTATS Christopher M. Bishop, John M. Winn
2002	—	`conf`	VIBES: A Variational Inference Engine for Bayesian Networks. NIPS Christopher M. Bishop, David J. Spiegelhalter, John M. Winn
2000	—	`conf`	Non-linear Bayesian Image Modelling. ECCV (1) Christopher M. Bishop, John M. Winn