Neural Expectation Maximization
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[1] P. Milner. A model for visual shape recognition. , 1974, Psychological review.
[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[3] New York Dover,et al. ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .
[4] PAUL J. WERBOS,et al. Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.
[5] H. B. Barlow,et al. Finding Minimum Entropy Codes , 1989, Neural Computation.
[6] Geoffrey E. Hinton,et al. Distributed Representations , 1986, The Philosophy of Artificial Intelligence.
[7] J. Urgen Schmidhuber. Learning to Control Fast-weight Memories: an Alternative to Dynamic Recurrent Networks , 1991 .
[8] Jürgen Schmidhuber,et al. Learning Complex, Extended Sequences Using the Principle of History Compression , 1992, Neural Computation.
[9] Jürgen Schmidhuber,et al. Learning Factorial Codes by Predictability Minimization , 1992, Neural Computation.
[10] Christoph von der Malsburg,et al. The Correlation Theory of Brain Function , 1994 .
[11] DeLiang Wang,et al. Locally excitatory globally inhibitory oscillator networks , 1995, IEEE Transactions on Neural Networks.
[12] C. Malsburg. Binding in models of perception and brain function , 1995, Current Opinion in Neurobiology.
[13] Eric Saund,et al. A Multiple Cause Mixture Model for Unsupervised Learning , 1995, Neural Computation.
[14] A. Treisman. The binding problem , 1996, Current Opinion in Neurobiology.
[15] Heiko Wersing,et al. A Competitive-Layer Model for Feature Binding and Sensory Segmentation , 2001, Neural Computation.
[16] Brendan J. Frey,et al. Learning flexible sprites in video layers , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[17] I. Daubechies,et al. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.
[18] Aapo Hyvärinen,et al. Learning to Segment Any Random Vector , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[19] Thomas Hofmann,et al. Clustering appearance and shape by learning jigsaws , 2007 .
[20] Eero P. Simoncelli,et al. Image denoising using mixtures of Gaussian scale mixtures , 2008, 2008 15th IEEE International Conference on Image Processing.
[21] Richard G. Baraniuk,et al. Sparse Coding via Thresholding and Local Competition in Neural Circuits , 2008, Neural Computation.
[22] A. Ravishankar Rao,et al. Unsupervised Segmentation With Dynamical Units , 2008, IEEE Transactions on Neural Networks.
[23] Marc Teboulle,et al. A fast Iterative Shrinkage-Thresholding Algorithm with application to wavelet-based image deblurring , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.
[24] James Bailey,et al. Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance , 2010, J. Mach. Learn. Res..
[25] Yann LeCun,et al. Learning Fast Approximations of Sparse Coding , 2010, ICML.
[26] A. Ravishankar Rao,et al. An objective function utilizing complex sparsity for efficient segmentation in multi-layer oscillatory networks , 2010, Int. J. Intell. Comput. Cybern..
[27] Nicolas Le Roux,et al. Learning a Generative Model of Images by Factoring Appearance and Shape , 2011, Neural Computation.
[28] Yoshua Bengio,et al. Deep Learning of Representations: Looking Forward , 2013, SLSP.
[29] Jonathan Le Roux,et al. Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures , 2014, ArXiv.
[30] Thomas Serre,et al. Neuronal Synchrony in Complex-Valued Deep Networks , 2013, ICLR.
[31] Guillermo Sapiro,et al. Learning Efficient Sparse and Low Rank Models , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Jürgen Schmidhuber,et al. Binding via Reconstruction Clustering , 2015, ArXiv.
[33] Edward H. Adelson,et al. Learning visual groups from co-occurrences in space and time , 2015, ArXiv.
[34] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[35] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[36] Harri Valpola,et al. Tagger: Deep Unsupervised Perceptual Grouping , 2016, NIPS.
[37] Vincent Dumoulin,et al. Deconvolution and Checkerboard Artifacts , 2016 .
[38] Trevor Darrell,et al. Learning Features by Watching Objects Move , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Alexander Ilin,et al. Recurrent Ladder Networks , 2017, NIPS.
[40] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[41] Cordelia Schmid,et al. SfM-Net: Learning of Structure and Motion from Video , 2017, ArXiv.