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All publications by Raquel Urtasun
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Learning Deep Structured Models
Liang-chieh Chen, Alexander Schwing, Alan Yuille and Raquel Urtasun
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


Skip-Thought Vectors
Ryan Kiros, Yukun Zhu, Ruslan R. Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba and Sanja Fidler
Advances in Neural Information Processing Systems 28, 2015


3D Object Proposals for Accurate Object Class Detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler and Raquel Urtasun
Advances in Neural Information Processing Systems 28, 2015


Globally Convergent Parallel MAP LP Relaxation Solver using the Frank-Wolfe Algorithm
Alexander Schwing, Tamir Hazan, Marc Pollefeys and Raquel Urtasun
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials
Shenlong Wang, Alex Schwing and Raquel Urtasun
Advances in Neural Information Processing Systems 27, 2014


Message Passing Inference for Large Scale Graphical Models with High Order Potentials
Jian Zhang, Alex Schwing and Raquel Urtasun
Advances in Neural Information Processing Systems 27, 2014


Latent Structured Active Learning
Wenjie Luo, Alex Schwing and Raquel Urtasun
Advances in Neural Information Processing Systems 26, 2013


Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins
Alex Schwing, Tamir Hazan, Marc Pollefeys and Raquel Urtasun
Advances in Neural Information Processing Systems 25, 2012


3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model
Sanja Fidler, Sven Dickinson and Raquel Urtasun
Advances in Neural Information Processing Systems 25, 2012


Efficient Structured Prediction with Latent Variables for General Graphical Models
Alexander Schwing, Tamir Hazan, Marc Pollefeys and Raquel Urtasun
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


A Family of MCMC Methods on Implicitly Defined Manifolds
Marcus A. Brubaker, Mathieu Salzmann and Raquel Urtasun
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Joint 3D Estimation of Objects and Scene Layout
Andreas Geiger, Christian Wojek and Raquel Urtasun
Advances in Neural Information Processing Systems 24, 2011


Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities
Angela Yao, Juergen Gall, Luc V. Gool and Raquel Urtasun
Advances in Neural Information Processing Systems 24, 2011


Convex Max-Product over Compact Sets for Protein Folding
Jian Peng, Tamir Hazan, Raquel Urtasun and David A. Mcallester
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Sparse Coding for Learning Interpretable Spatio-Temporal Primitives
Taehwan Kim, Gregory Shakhnarovich and Raquel Urtasun
Advances in Neural Information Processing Systems 23, 2010


A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction
Tamir Hazan and Raquel Urtasun
Advances in Neural Information Processing Systems 23, 2010


Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation
Mathieu Salzmann and Raquel Urtasun
Advances in Neural Information Processing Systems 23, 2010


Factorized Orthogonal Latent Spaces
Mathieu Salzmann, Carl H. Ek, Raquel Urtasun and Trevor Darrell
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Non-linear matrix factorization with Gaussian processes
Neil D. Lawrence and Raquel Urtasun
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Topologically-constrained latent variable models
Raquel Urtasun, David J. Fleet, Andreas Geiger, Jovan Popovic, Trevor Darrell and Neil D. Lawrence
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Discriminative Gaussian process latent variable model for classification
Raquel Urtasun and Trevor Darrell
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007