Final thesis for the fulfilment of the requirements for the degree of MSc. in Applied Computing Science Improving Score Matching for learning statistical models of natural images
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[1] Aapo Hyvärinen,et al. Estimating Markov Random Field Potentials for Natural Images , 2009, ICA.
[2] Michael J. Black,et al. Fields of Experts , 2009, International Journal of Computer Vision.
[3] Aapo Hyvärinen,et al. Optimal Approximation of Signal Priors , 2008, Neural Computation.
[4] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[5] Marc'Aurelio Ranzato,et al. Sparse Feature Learning for Deep Belief Networks , 2007, NIPS.
[6] Aapo Hyvärinen,et al. A Two-Layer ICA-Like Model Estimated by Score Matching , 2007, ICANN.
[7] Aapo Hyvärinen,et al. Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables , 2007, IEEE Transactions on Neural Networks.
[8] William T. Freeman,et al. What makes a good model of natural images? , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Aapo Hyvärinen,et al. Some extensions of score matching , 2007, Comput. Stat. Data Anal..
[10] Eero P. Simoncelli,et al. Statistical Modeling of Images with Fields of Gaussian Scale Mixtures , 2006, NIPS.
[11] Michael J. Black,et al. Denoising Archival Films using a Learned Bayesian Model , 2006, 2006 International Conference on Image Processing.
[12] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[13] Yee Whye Teh,et al. Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation , 2006, Cogn. Sci..
[14] Charles Kervrann,et al. Unsupervised Patch-Based Image Regularization and Representation , 2006, ECCV.
[15] Fu Jie Huang,et al. A Tutorial on Energy-Based Learning , 2006 .
[16] Peter V. Gehler,et al. Products of Edge-perts , 2005, NIPS.
[17] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[18] Michael J. Black,et al. Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[19] Jean-Michel Morel,et al. A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..
[20] Geoffrey E. Hinton,et al. Exponential Family Harmoniums with an Application to Information Retrieval , 2004, NIPS.
[21] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[22] Eero P. Simoncelli,et al. Image Denoising using Gaussian Scale Mixtures in the Wavelet Domain , 2002 .
[23] Jitendra Malik,et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[24] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[25] Edward E. Smith,et al. An Invitation to cognitive science , 1997 .
[26] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[27] Song-Chun Zhu,et al. Prior Learning and Gibbs Reaction-Diffusion , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[28] Joachim Weickert,et al. A Review of Nonlinear Diffusion Filtering , 1997, Scale-Space.
[29] Terrence J. Sejnowski,et al. Edges are the Independent Components of Natural Scenes , 1996, NIPS.
[30] Jürgen Schmidhuber,et al. Semilinear Predictability Minimization Produces Well-Known Feature Detectors , 1996, Neural Computation.
[31] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[32] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[33] S. Mitter,et al. On sampling methods and annealing algorithms , 1990 .
[34] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[35] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[36] George M. Siouris,et al. Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.
[37] Julius. Nelson. A Study of Dreams , 1888 .