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Gaussian-binary restricted Boltzmann machines for modeling natural image statistics

Jan Melchior, Nan Wang, Laurenz Wiskott, Holger Fröhlich
2017 PLoS ONE  
We present a theoretical analysis of Gaussian-binary restricted Boltzmann machines (GRBMs) from the perspective of density models.  ...  We show that GRBMs are capable of learning meaningful features both in a two-dimensional blind source separation task and in modeling natural images.  ...  The resulting model is known as Gaussian-binary restricted Boltzmann machines (GRBMs) or Gaussian-Bernoulli restricted Boltzmann machines [7] [8] [9] .  ... 
doi:10.1371/journal.pone.0171015 pmid:28152552 pmcid:PMC5289828 fatcat:ighnfkg6wjeylds5krfyauzjji

An analysis of Gaussian-binary restricted Boltzmann machines for natural images

Nan Wang, Jan Melchior, Laurenz Wiskott
2012 The European Symposium on Artificial Neural Networks  
A Gaussian-binary restricted Boltzmann machine is a widely used energy-based model for continuous data distributions, although many authors reported difficulties in training on natural images.  ...  Based on this formula we show how Gaussian-binary RBMs learn natural image statistics. However the probability density function of the model is not a good representation of the data distribution.  ...  Introduction In this paper we present an analysis of Gaussian-binary restricted Boltzmann machines (GB-RBMs) from the density estimation perspective and from the particular perspective of modeling natural  ... 
dblp:conf/esann/WangMW12 fatcat:u7yyiythijea7lj6vtff5xxgii

A Spike and Slab Restricted Boltzmann Machine

Aaron C. Courville, James Bergstra, Yoshua Bengio
2011 Journal of machine learning research  
We illustrate how the spike and slab Restricted Boltzmann Machine achieves competitive performance on the CIFAR-10 object recognition task.  ...  mean and covariance Restricted Boltzmann Machine.  ...  Acknowledgements We acknowledge NSERC, FQRNT, RQCHP and Compute Canada for their financial and computational support; Chris Williams for pointing out the connection between the PoPPCA model and the ssRBM  ... 
dblp:journals/jmlr/CourvilleBB11 fatcat:yo6hkv2qynfi5cthrgz3kx7whq

Boltzmann Machine and its Applications in Image Recognition [chapter]

Shifei Ding, Jian Zhang, Nan Zhang, Yanlu Hou
2016 IFIP Advances in Information and Communication Technology  
Weight uncertainty spike-and-slab deep Boltzmann Machine ssRBM is used to model nature images.  ...  In Gaussian-binary RBM (mRBM) [15] , the conditional probabilities of visible units follow Gaussian distribution. However, the mRBM performs not well in modeling nature images.  ...  Conclusion In this paper, in order to alleviate the overfitting problem, and improve the ability of image reconstruction in RBM model, we introduce the Weight uncertainty method to RBM.  ... 
doi:10.1007/978-3-319-48390-0_12 fatcat:oa5eflljlzczzic7lcvfe2fioe

Thurstonian Boltzmann Machines: Learning from Multiple Inequalities [article]

Truyen Tran, Dinh Phung, Svetha Venkatesh
2014 arXiv   pre-print
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time.  ...  Our proposed TBM naturally supports the following types: Gaussian, intervals, censored, binary, categorical, muticategorical, ordinal, (in)-complete rank with and without ties.  ...  Our framework utilises the Gaussian restricted Boltzmann machines, but the Gaussian variables are never observed except for one limiting case.  ... 
arXiv:1408.0055v1 fatcat:4y3vqsvwlrb4jjtoovk7yxxcc4

Robust Deep Appearance Models [article]

Kha Gia Quach, Chi Nhan Duong, Khoa Luu, Tien D. Bui
2016 arXiv   pre-print
The two models are connected by Restricted Boltzmann Machines at the top layer to jointly learn and capture the variations of both facial shapes and appearances.  ...  In this approach, two crucial components of face images, i.e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively.  ...  ACKNOWLEDGMENT This work is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada  ... 
arXiv:1607.00659v1 fatcat:hisxvnqt7rebbf5pb2ap7dnvaa

Boltzmann Machines and Denoising Autoencoders for Image Denoising [article]

Kyunghyun Cho
2013 arXiv   pre-print
In this paper, we propose that another popular family of models in the field of deep learning, called Boltzmann machines, can perform image denoising as well as, or in certain cases of high level of noise  ...  Image denoising based on a probabilistic model of local image patches has been employed by various researchers, and recently a deep (denoising) autoencoder has been proposed by Burger et al. [2012] and  ...  Boltzmann machines We consider a BM with a set of Gaussian visible units v that correspond to the pixels of an image patch and a set of binary hidden units h.  ... 
arXiv:1301.3468v6 fatcat:ubupuw5xpvdtlhfj546bhlwsta

Deep Belief Nets [chapter]

Geoffrey Hinton
2014 Encyclopedia of Machine Learning and Data Mining  
Restricted Boltzmann Machines (Smolensky ,1986 , called them "harmoniums") • We restrict the connectivity to make learning easier. -Only one layer of hidden units. !  ...  First, model the distribution of digit images 2000 units 500 units 500 units 28 x 28 pixel image The top two layers form a restricted Boltzmann machine whose free energy landscape should  ...  Using conditional higher-order Boltzmann machines to model image transformations (Memisevic and Hinton, 2007) • A transformation unit specifies which pixel goes to which other pixel.  ... 
doi:10.1007/978-1-4899-7502-7_67-1 fatcat:2ql4ea3d5bdijbpszbtcoz3dmm

Deep Belief Nets [chapter]

Geoffrey Hinton
2017 Encyclopedia of Machine Learning and Data Mining  
Restricted Boltzmann Machines (Smolensky ,1986 , called them "harmoniums") • We restrict the connectivity to make learning easier. -Only one layer of hidden units. !  ...  First, model the distribution of digit images 2000 units 500 units 500 units 28 x 28 pixel image The top two layers form a restricted Boltzmann machine whose free energy landscape should  ...  Using conditional higher-order Boltzmann machines to model image transformations (Memisevic and Hinton, 2007) • A transformation unit specifies which pixel goes to which other pixel.  ... 
doi:10.1007/978-1-4899-7687-1_67 fatcat:pyaniz4darcstmtzswyfq56sxe

Multimodal Learning with Deep Boltzmann Machines

Nitish Srivastava, Ruslan Salakhutdinov
2012 Neural Information Processing Systems  
A Deep Boltzmann Machine is described for learning a generative model of data that consists of multiple and diverse input modalities.  ...  Our experimental results on bi-modal data consisting of images and text show that the Multimodal DBM can learn a good generative model of the joint space of image and text inputs that is useful for information  ...  Restricted Boltzmann Machines A Restricted Boltzmann Machine is an undirected graphical model with stochastic visible units v ∈ {0, 1} D and stochastic hidden units h ∈ {0, 1} F , with each visible unit  ... 
dblp:conf/nips/SrivastavaS12 fatcat:dnhlapisb5fpvn322psjhews6e

Transformation Equivariant Boltzmann Machines [chapter]

Jyri J. Kivinen, Christopher K. I. Williams
2011 Lecture Notes in Computer Science  
Extending prior work on translation equivariant (convolutional) models, we develop translation and rotation equivariant restricted Boltzmann machines (RBMs) and deep belief nets (DBNs), and demonstrate  ...  their effectiveness in learning frequently occurring statistical structure from artificial and natural images.  ...  Building in Transformation Equivariance We first discuss the rotation-equivariant restricted Boltzmann machine (STEER-RBM) model which has one hidden layer; this hidden layer contains the 'steerable' units  ... 
doi:10.1007/978-3-642-21735-7_1 fatcat:4uslxcddezh2pfk66w5drpkrxq

Visual Object Tracking Based on Cross-Modality Gaussian-Bernoulli Deep Boltzmann Machines with RGB-D Sensors

Mingxin Jiang, Zhigeng Pan, Zhenzhou Tang
2017 Sensors  
In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors.  ...  Visual object tracking technology is one of the key issues in computer vision.  ...  Restricted Boltzmann Machine Setting both 0  L and 0  R in Equation (1), we will recover the model of a restricted Boltzmann machine (RBM), as shown in Figure 2 .  ... 
doi:10.3390/s17010121 pmid:28075373 pmcid:PMC5298694 fatcat:gp7ut2b63zhsthru5fcah3lz6a

Multiple Texture Boltzmann Machines

Jyri J. Kivinen, Christopher K. I. Williams
2012 Journal of machine learning research  
restricted Boltzmann machine (GB-RBM).  ...  We assess the generative power of the mPoTmodel of [10] with tiled-convolutional weight sharing as a model for visual textures by specifically training on this task, evaluating model performance on texture  ...  Introduction We consider the statistical modelling of natural images using Boltzmann machines.  ... 
dblp:journals/jmlr/KivinenW12 fatcat:ercodete5bfehkej6jtagmywg4

Feature Learning Using Bayesian Linear Regression Model

Siqi Nie, Qiang Ji
2014 2014 22nd International Conference on Pattern Recognition  
Currently one of the dominant approaches is the restricted Boltzmann machine (RBM).  ...  This model can also be denoted as Factor analysis, which is a statistical method for modeling the covariance structure of high dimensional data, but has not been used for feature learning.  ...  A typical latent variable density model for feature learning is the Gaussian restricted Boltzmann machine (GRBM) [5] .  ... 
doi:10.1109/icpr.2014.267 dblp:conf/icpr/NieJ14 fatcat:bfsnv2yv7zgibnkxkc2wudcqju

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton
2010 Journal of machine learning research  
The Gaussian-Binary RBMs that have been used to model real-valued data are not a good way to model the covariance structure of natural images.  ...  The problem lies in the restricted Boltzmann machine (RBM) which is used as a module for learning deep belief nets one layer at a time.  ...  DBNs were first developed for binary data using a Restricted Boltzmann Machine (RBM) as the basic module for learning each layer (Hinton et al., 2006a) .  ... 
dblp:journals/jmlr/RanzatoKH10 fatcat:aexbp4kwpberfez5zr662pbnpi
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