Learning Deep Structured Models
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Alan L. Yuille | Raquel Urtasun | Alexander G. Schwing | Liang-Chieh Chen | A. Yuille | Liang-Chieh Chen | A. Schwing | R. Urtasun
[1] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[2] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[3] Paul Tseng,et al. Relaxation methods for problems with strictly convex separable costs and linear constraints , 1987, Math. Program..
[4] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[5] John S. Bridle,et al. Training Stochastic Model Recognition Algorithms as Networks can Lead to Maximum Mutual Information Estimation of Parameters , 1989, NIPS.
[6] Yoshua Bengio,et al. Global training of document processing systems using graph transformer networks , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[7] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[8] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[9] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[10] Tom Heskes,et al. Fractional Belief Propagation , 2002, NIPS.
[11] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[12] Ben Taskar,et al. Learning structured prediction models: a large margin approach , 2005, ICML.
[13] William T. Freeman,et al. Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.
[14] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[15] Martin J. Wainwright,et al. A new class of upper bounds on the log partition function , 2002, IEEE Transactions on Information Theory.
[16] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[17] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[18] Tommi S. Jaakkola,et al. Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations , 2007, NIPS.
[19] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[20] Yair Weiss,et al. MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies , 2007, UAI.
[21] Tommi S. Jaakkola,et al. Tightening LP Relaxations for MAP using Message Passing , 2008, UAI.
[22] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[23] Eric Fosler-Lussier,et al. Conditional Random Fields for Integrating Local Discriminative Classifiers , 2008, IEEE Transactions on Audio, Speech, and Language Processing.
[24] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[25] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[26] J. Schmidhuber,et al. A Novel Connectionist System for Unconstrained Handwriting Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Amir Globerson,et al. Convergent message passing algorithms - a unifying view , 2009, UAI.
[28] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[29] Manik Varma,et al. Character Recognition in Natural Images , 2009, VISAPP.
[30] Jian Peng,et al. Conditional Neural Fields , 2009, NIPS.
[31] Tommi S. Jaakkola,et al. Learning Efficiently with Approximate Inference via Dual Losses , 2010, ICML.
[32] Eric Fosler-Lussier,et al. Backpropagation training for multilayer conditional random field based phone recognition , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.
[33] Tamir Hazan,et al. Norm-Product Belief Propagation: Primal-Dual Message-Passing for Approximate Inference , 2009, IEEE Transactions on Information Theory.
[34] Thierry Artières,et al. Neural conditional random fields , 2010, AISTATS.
[35] Tamir Hazan,et al. A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction , 2010, NIPS.
[36] Andrew Y. Ng,et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.
[37] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[38] Sebastian Nowozin,et al. Decision tree fields , 2011, 2011 International Conference on Computer Vision.
[39] Sanja Fidler,et al. Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[40] Marc Pollefeys,et al. Efficient Structured Prediction with Latent Variables for General Graphical Models , 2012, ICML.
[41] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[42] Jian Peng,et al. A conditional neural fields model for protein threading , 2012, Bioinform..
[43] Andrew Y. Ng,et al. Convolutional-Recursive Deep Learning for 3D Object Classification , 2012, NIPS.
[44] Geoffrey E. Hinton,et al. An Efficient Learning Procedure for Deep Boltzmann Machines , 2012, Neural Computation.
[45] Sebastian Nowozin,et al. Regression Tree Fields — An efficient, non-parametric approach to image labeling problems , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[46] Justin Domke,et al. Structured Learning via Logistic Regression , 2013, NIPS.
[47] Alexander Gerhard Schwing,et al. Inference and learning algorithms with applications to 3D indoor scene understanding , 2013 .
[48] Geoffrey E. Hinton,et al. Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[49] Sebastian Nowozin,et al. Learning Convex QP Relaxations for Structured Prediction , 2013, ICML.
[50] Jonathan Tompson,et al. Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation , 2014, NIPS.
[51] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[52] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[53] Jitendra Malik,et al. Simultaneous Detection and Segmentation , 2014, ECCV.
[54] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[55] Jia Xu,et al. Tell Me What You See and I Will Show You Where It Is , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[56] Samy Bengio,et al. Large-Scale Object Classification Using Label Relation Graphs , 2014, ECCV.
[57] Yann LeCun,et al. Understanding Deep Architectures using a Recursive Convolutional Network , 2013, ICLR.
[58] Richard S. Zemel,et al. High Order Regularization for Semi-Supervised Learning of Structured Output Problems , 2014, ICML.
[59] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[60] Raquel Urtasun,et al. Fully Connected Deep Structured Networks , 2015, ArXiv.
[61] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).