Mixture Representations for Inference and Learning in Boltzmann Machines.

We present results for both inference and learning to demonstrate the effectiveness of this approach. ... Boltzmann machines are undirected graphical models with two-state stochastic variables, in which the logarithms of the clique potentials are quadratic functions of the node states. ... Acknowledgements We are grateful to Tommi Jaakkola for helpful com ments. Also, we would like to thank David MacKay for a stimulating discussion about mean field theory . ...

arXiv:1301.7393v1 fatcat:hkyweq5f5rhdblbtnppx7d77pq

A graphical model has both a str~lctural con~ponent-encoded L7p the pattern oI edges in the gsapl1-and a paramctr-ic compo~ient-encoded by ~iumcl-ical "potentials" associated with sets of cdgcs in thc ... In particular, gencral i r~f i~r o~c c~ ( 7 1~0 -ritlr~~rs allow statistical quantities (such as likelihoods a n d conditional probabilities) and information-theoretic quantities (such as mutual infor ... The inference and leal-ning algorithms for Boltzmann ~nachines, a n d in particular the treatment of hidden units, ex&nrplify nioi-c gawral solw tions to the problem of inference and learn in^ in graphic ...

doi:10.1007/s100440200036 fatcat:bt75wlwba5hefifledkf62lv4e

The generative models that are most familiar in statistics and machine learning are the ones for which the posterior distribution can be inferred efficiently and exactly because the model has been strongly ... Consider, for example, a mixture of gaussians model in which each data-vector is assumed to come from exactly one of the multivariate gaussian distributions in the mixture. ... Acknowledgements I thank Yoshua Bengio, David MacKay, Terry Sejnowski and my past and present postdoctoral fellows and graduate students for helping me to understand these ideas, and NSERC, CIAR, CFI and ...

doi:10.1016/j.tics.2007.09.004 pmid:17921042 fatcat:nulwaexkjreh5h7udl4bezd24a

To Abstract Restricted Boltzmann Machines, Deep Boltzmann Machines, and their extensions have brought much attention and become powerful tools for many machine learning tasks. ... In this paper, we aim to give a review of recent developments of such models for sequential data modelling. ... Notice that the bias terms for visbile and hidden units are ignored in Eqn. (10) for simplifying the representation. ...

doi:10.26717/bjstr.2017.01.000336 fatcat:mg4czqdceng7viogpu5b4dzlg4

After this, we consider various structures used in deep learning, including restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and nonlinear autoencoders. ... Deep neural networks with several layers have during the last years become a highly successful and popular research topic in machine learning due to their excellent performance in many benchmark problems ... Differences between Deep Belief Networks and Deep Boltzmann Machines Thus far we have discussed only learning, but not using these networks for inference and generation of new samples. ...

doi:10.1016/b978-0-12-802806-3.00007-5 fatcat:35unnrrzjbdapgqrpcn4d7vnem

While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. ... Image denoising and inpainting correspond to posterior inference in the RoBM. ... We have described a novel model which allows Boltzmann Machines to be robust to noise and occlusions. ...

doi:10.1109/cvpr.2012.6247936 dblp:conf/cvpr/TangSH12 fatcat:ll4c4ofpbjgj5dt6qocna3vwuu

One of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. ... Combining this initialization method with a new method for finetuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights ... Summary of the main story In about 1986, backpropagation replaced the Boltzmann machine learning algorithm as the method of choice for learning distributed representations. ...

doi:10.1111/cogs.12049 pmid:23800216 fatcat:gsqrcryoazeivp2vjr44n6mv2q

Szczepanski

To address this problem, we propose a point-wise gated Boltzmann machine, a unified generative model that combines feature learning and feature selection. ... Unsupervised feature learning has emerged as a promising tool in learning representations from unlabeled data. ... Acknowledgments This work was supported in part by NSF IIS 1247414 and a Google Faculty Research Award. ...

dblp:conf/icml/SohnZLL13 fatcat:4xcsch3op5ahjndycwqr5bsvxu

We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. ... We present results on the MNIST and NORB datasets showing that deep Boltzmann machines learn good generative models and perform well on handwritten digit and visual object recognition tasks. ... Acknowledgments We thank Vinod Nair for sharing his code for blurring and translating NORB images. This research was supported by NSERC and Google. ...

dblp:journals/jmlr/SalakhutdinovH09 fatcat:syxpyp5uw5dsdb2rmbnkaswufq

Szczepanski

In this work, we propose a novel visual codebook learning approach using the restricted Boltzmann machine (RBM) as our generative model. Our contribution is three-fold. ... Firstly, we steer the unsupervised RBM learning using a regularization scheme, which decomposes into a combined prior for the sparsity of each feature's representation as well as the selectivity for each ... Fig Fig. 1 shows our BoW framework using restricted Boltzmann machines (RBM) to learn visual codes and perform feature coding during inference. ...

doi:10.1007/978-3-642-33715-4_22 fatcat:vjoe6a7qlrdoxhlm424gnptu6y

The SDN automatically learns hierarchical features, salience maps, and mixture representations of different body parts. ... At the part and body levels, it is able to infer the most appropriate template for the mixture models of each part and the whole body. ... The authors would like to thank Wanli Ouyang and Xingyu Zeng for helpful suggestion, and Yonglong Tian for his main contribution in experiments and part of the derivation of the model. ...

doi:10.1109/cvpr.2014.120 dblp:conf/cvpr/LuoTWT14 fatcat:2ooo25dkbjhkjgd3or2crzjuci

We present the Factorial Hidden Restricted Boltzmann Machine (FHRBM) for robust speech recognition. ... to learn a partsbased representation of noisy speech data that can generalize better to previously unseen noise compositions. ... INTRODUCTION Restricted Boltzmann machines (RBMs) have recently been applied to several well established problems in machine learning and signal processing, with great success. ...

doi:10.1109/icassp.2012.6288869 dblp:conf/icassp/RennieFD12 fatcat:cbfbcdys55eghi3rxut63z5pdy

The method is similar to Boltzmann machine learn- ing, but without the “negative phase.” Lack of a negative phase allows learning to proceed significantly faster than in a Boltzmann machine. ... Mixture models and hidden Neural Computation 4, 832-834 (1992) © 1992 Massachusetts Institute of Technology Asymmetric Parallel Boltzmann Machines ic accident gul W 1 oOo 0 Z pheumonia L <— ae NS A Ba ...

doi:10.1162/neco.1992.4.6.832 fatcat:mfjzujcjqnchhpfwxzb6bcdxoe

This paper presents a novel feature learning method for visual speech recognition using Deep Boltzmann Machines (DBM). ... features and can be adopted by other deep learning systems. ... In this paper, we use Deep Boltzmann Machines (DBM) [18] to learn the visual features. ...

doi:10.1109/iccv.2015.26 dblp:conf/iccv/SuiBT15 fatcat:7pulo4baavccxmi6dxltrr6zfm

Variational methods are becoming increasingly popular for inference and learning in probabilistic models. ... In this paper we review the underlying framework of variational methods and discuss example applications involving sigmoid belief networks, Boltzmann machines and feed-forward neural networks. ... Acknowledgements I would like to thank Brendan Frey, Tommi Jaakkola, Michael Jordan, Neil Lawrence, David MacKay and Michael Tipping for helpful discussions regarding variational methods. ...

doi:10.1007/978-1-4471-1599-1_2 fatcat:wwba75whkneo7fvdaf75xvuu44

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