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Gaussian-binary restricted Boltzmann machines for modeling natural image statistics
2017
PLoS ONE
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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
2012
The European Symposium on Artificial Neural Networks
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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
2011
Journal of machine learning research
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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]
2016
IFIP Advances in Information and Communication Technology
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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]
2014
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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]
2013
arXiv
pre-print
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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]
2014
Encyclopedia of Machine Learning and Data Mining
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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]
2017
Encyclopedia of Machine Learning and Data Mining
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The file type is application/pdf.
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
2012
Neural Information Processing Systems
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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]
2011
Lecture Notes in Computer Science
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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
2017
Sensors
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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
2012
Journal of machine learning research
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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
2014
2014 22nd International Conference on Pattern Recognition
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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
2010
Journal of machine learning research
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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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