메뉴 건너뛰기




Volumn 3, Issue , 2015, Pages 1785-1794

Learning deep structured models

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES;

EID: 84969930631     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (169)

References (51)
  • 2
    • 0030648914 scopus 로고    scopus 로고
    • Global training of document processing systems using graph transformer networks
    • Bottou, L., Bengio, Y., and LeCun, Y. Global training of document processing systems using graph transformer networks. In Proc. CVPR, 1997.
    • (1997) Proc. CVPR
    • Bottou, L.1    Bengio, Y.2    LeCun, Y.3
  • 3
    • 0001699291 scopus 로고
    • Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters
    • Bridle, J. S. Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters. In Proc. NIPS, 1990.
    • (1990) Proc. NIPS
    • Bridle, J.S.1
  • 4
    • 85083954148 scopus 로고    scopus 로고
    • Semantic image segmentation with deep convolutional nets and fully connected CRFs
    • Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. In Proc. ICLR, 2015.
    • (2015) Proc. ICLR
    • Chen, L.-C.1    Papandreou, G.2    Kokkinos, I.3    Murphy, K.4    Yuille, A.L.5
  • 10
    • 84898956042 scopus 로고    scopus 로고
    • Structured learning via logistic regression
    • Domke, J. Structured Learning via Logistic Regression. In Proc. NIPS, 2013.
    • (2013) Proc. NIPS
    • Domke, J.1
  • 11
    • 85083953781 scopus 로고    scopus 로고
    • Understanding deep architectures using a recursive convolutional network
    • Eigen, D., Rolfe, J., Fergus, R., and LeCun, Y. Understanding Deep Architectures using a Recursive Convolutional Network. In Proc. ICLR, 2014.
    • (2014) Proc. ICLR
    • Eigen, D.1    Rolfe, J.2    Fergus, R.3    LeCun, Y.4
  • 12
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • Girshick, R., Donahue, J., Darrell, T., and Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. CVPR, 2014.
    • (2014) Proc. CVPR
    • Girshick, R.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 13
    • 84875745284 scopus 로고    scopus 로고
    • Fixing max-product: Convergent message passing algorithms for MAP LP-relaxations
    • Globerson, A. and Jaakkola, T. Fixing max-product: Convergent message passing algorithms for MAP LP-relaxations. In Proc. NIPS, 2007.
    • (2007) Proc. NIPS
    • Globerson, A.1    Jaakkola, T.2
  • 15
    • 78649384136 scopus 로고    scopus 로고
    • Norm-product belief propagation: Primal-dual message-passing for LP-relaxation and approximate-inference
    • Hazan, T. and Shashua, A. Norm-Product Belief Propagation: Primal-Dual Message-Passing for LP-Relaxation and Approximate-Inference. Trans. Information Theory, 2010.
    • (2010) Trans. Information Theory
    • Hazan, T.1    Shashua, A.2
  • 16
    • 85162009902 scopus 로고    scopus 로고
    • A primal-dual message-passing algorithm for approximated large scale structured prediction
    • Hazan, T. and Urtasun, R. A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction. In Proc. NIPS, 2010.
    • (2010) Proc. NIPS
    • Hazan, T.1    Urtasun, R.2
  • 17
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science, 2006.
    • (2006) Science
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 19
    • 84866674114 scopus 로고    scopus 로고
    • Regression tree fields - an efficient, non-parametric approach to image labeling problems
    • Jancsary, J., Nowozin, S., Sharp, T., and Rother, C. Regression Tree Fields - An Efficient, Non-parametric Approach to Image Labeling Problems. In Proc. CVPR, 2012.
    • (2012) Proc. CVPR
    • Jancsary, J.1    Nowozin, S.2    Sharp, T.3    Rother, C.4
  • 20
    • 84897567935 scopus 로고    scopus 로고
    • Learning convex QP relaxations for structured prediction
    • Jancsary, J., Nowozin, S., and Rother, C. Learning Convex QP Relaxations for Structured Prediction. In Proc. ICML, 2013.
    • (2013) Proc. ICML
    • Jancsary, J.1    Nowozin, S.2    Rother, C.3
  • 23
    • 84889603377 scopus 로고    scopus 로고
    • Im-ageNet classification with deep convolutional neural networks
    • Krizhevsky, A., Sutskever, I., and Hinton, G. E. Im-ageNet Classification with Deep Convolutional Neural Networks. In Proc. NIPS, 2013.
    • (2013) Proc. NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 24
    • 50249093806 scopus 로고    scopus 로고
    • An empirical evaluation of deep architectures on problems with many factors of variation
    • Larochelle, H., Erhan, D., Courville, A., Bergstra, J., and Bengio, Y. An empirical evaluation of deep architectures on problems with many factors of variation. In Proc. ICML, 2007.
    • (2007) Proc. ICML
    • Larochelle, H.1    Erhan, D.2    Courville, A.3    Bergstra, J.4    Bengio, Y.5
  • 26
    • 71149119164 scopus 로고    scopus 로고
    • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    • Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proc. ICML, 2009.
    • (2009) Proc. ICML
    • Lee, H.1    Grosse, R.2    Ranganath, R.3    Ng, A.Y.4
  • 27
    • 84919825360 scopus 로고    scopus 로고
    • High order regularization for semi-supervised learning of structured output problems
    • Li, Y. and Zemel, R. High Order Regularization for Semi-Supervised Learning of Structured Output Problems. In Proc. ICML, 2014.
    • (2014) Proc. ICML
    • Li, Y.1    Zemel, R.2
  • 28
    • 84863519768 scopus 로고    scopus 로고
    • A conditional neural fields model for protein threading
    • Ma, J., Peng, J., Wang, S., and Xu, J. A conditional neural fields model for protein threading. Bioinformatics, 2012.
    • (2012) Bioinformatics
    • Ma, J.1    Peng, J.2    Wang, S.3    Xu, J.4
  • 29
    • 80053164534 scopus 로고    scopus 로고
    • Convergent message passing algorithms: A unifying view
    • Meltzer, T., Globerson, A., and Weiss, Y. Convergent Message Passing Algorithms: a unifying view. In Proc. UAI, 2009.
    • (2009) Proc. UAI
    • Meltzer, T.1    Globerson, A.2    Weiss, Y.3
  • 30
    • 77956556288 scopus 로고    scopus 로고
    • Learning efficiently with approximate inference via dual losses
    • Meshi, O., Sontag, D., Jaakkola, T., and Globerson, A. Learning Efficiently with Approximate inference via Dual Losses. In Proc. ICML, 2010.
    • (2010) Proc. ICML
    • Meshi, O.1    Sontag, D.2    Jaakkola, T.3    Globerson, A.4
  • 34
    • 84863373241 scopus 로고    scopus 로고
    • Conditional neural fields
    • Peng, J., Bo, L., and Xu, J. Conditional Neural Fields. In Proc. NIPS, 2009.
    • (2009) Proc. NIPS
    • Peng, J.1    Bo, L.2    Xu, J.3
  • 35
    • 78049406405 scopus 로고    scopus 로고
    • Backpropagation training for multilayer conditional random field based phone recognition
    • Prabhavalkar, R. and Fosler-Lussier, E. Backpropagation training for multilayer conditional random field based phone recognition. In Proc. ICASSP, 2010.
    • (2010) Proc. ICASSP
    • Prabhavalkar, R.1    Fosler-Lussier, E.2
  • 36
    • 84874125782 scopus 로고    scopus 로고
    • An efficient learning procedure for deep boltzmann machines
    • Salakhutdinov, R. R. and Hinton, G. E. An Efficient Learning Procedure for Deep Boltzmann Machines. Neural Computation, 2012.
    • (2012) Neural Computation
    • Salakhutdinov, R.R.1    Hinton, G.E.2
  • 39
    • 84867113207 scopus 로고    scopus 로고
    • Efficient structured prediction with latent variables for general graphical models
    • Schwing, A. G., Hazan, T., Pollefeys, M., and Urtasun, R. Efficient Structured Prediction with Latent Variables for General Graphical Models. In Proc. ICML, 2012.
    • (2012) Proc. ICML
    • Schwing, A.G.1    Hazan, T.2    Pollefeys, M.3    Urtasun, R.4
  • 40
    • 84877789646 scopus 로고    scopus 로고
    • Convolutional-recursive deep learning for 3D object classification
    • Socher, R., Huval, B., Bhat, B., Manning, C. D., and Ng, A. Y. Convolutional-Recursive Deep Learning for 3D Object Classification. In Proc. NIPS, 2012.
    • (2012) Proc. NIPS
    • Socher, R.1    Huval, B.2    Bhat, B.3    Manning, C.D.4    Ng, A.Y.5
  • 43
    • 84930634156 scopus 로고    scopus 로고
    • Joint training of a convolutional network and a graphical model for human pose estimation
    • Tompson, J., Jain, A., LeCun, Y., and Bregler, C. Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation. In Proc. NIPS, 2014.
    • (2014) Proc. NIPS
    • Tompson, J.1    Jain, A.2    LeCun, Y.3    Bregler, C.4
  • 46
    • 80053218745 scopus 로고    scopus 로고
    • MAP estimation, linear programming and belief propagation with convex free energies
    • Weiss, Y., Yanover, C., and Meltzer, T. MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies. In Proc. UAI, 2007.
    • (2007) Proc. UAI
    • Weiss, Y.1    Yanover, C.2    Meltzer, T.3
  • 48
    • 84911404516 scopus 로고    scopus 로고
    • Tell me what you see and I will show you where it is
    • Xu, J., Schwing, A. G., and Urtasun, R. Tell me what you see and I will show you where it is. In Proc. CVPR, 2014.
    • (2014) Proc. CVPR
    • Xu, J.1    Schwing, A.G.2    Urtasun, R.3
  • 49
    • 23744513375 scopus 로고    scopus 로고
    • Constructing free-energy approximations and generalized belief propagation algorithms
    • Yedidia, J. S., Freeman, W. T., and Weiss, Y. Constructing free-energy approximations and generalized belief propagation algorithms. Trans. Information Theory, 2005.
    • (2005) Trans. Information Theory
    • Yedidia, J.S.1    Freeman, W.T.2    Weiss, Y.3
  • 50
    • 84921476116 scopus 로고    scopus 로고
    • Visualizing and understanding convolutional networks
    • Zeiler, M. D. and Fergus, R. Visualizing and Understanding Convolutional Networks. In Proc. ECCV, 2014.
    • (2014) Proc. ECCV
    • Zeiler, M.D.1    Fergus, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.