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An Introduction to Variational Methods for Graphical Models
[chapter]
1998
Learning in Graphical Models
Preserved Fulltext
This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). ...
We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. ...
Acknowledgments We wish to thank Brendan Frey, David Heckerman, Uffe Kjaerulff, and (as always) Peter Dayan for helpful comments on the manuscript.
Notes ...
doi:10.1007/978-94-011-5014-9_5
fatcat:jtcxyl7wvfeflb2h7uacbygriy
Advanced mean field methods: theory and practice
2002
Neurocomputing
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At this level, the 1999 NIPS Workshop on Advanced Mean Field Methods -organized by Manfred Opper and David Saad -was an outstanding success. ...
share a commitment to replacing the exact joint distribution by one which admits some simplification. ...
Similarly, there are good arguments for relocating the tutorial on variational methods and graphical models (chapter 10) to follow the statistical physics tutorials of chapters 2 and 3. ...
doi:10.1016/s0925-2312(02)00612-4
fatcat:f74bqzlgujgr7buxeuvi6nsxk4
An Introduction to Optimization Techniques in Computer Graphics
[article]
2014
Eurographics State of the Art Reports
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Scope and Intended Audience For this purpose, we propose an introductory course on optimization techniques in computer graphics. ...
On the other end, our goal is to lead up to current research including modern ideas such as compressed sensing, convex variational formulations, and sparsity-inducing norms. ...
Goldluecke Variational Methods, Eurographics 2014 Tutorial "Optimization Techniques in Computer Graphics" 96 1 Introduction to variational methods
2 Convex optimization primer: proximal gradient methods ...
doi:10.2312/egt.20141019
fatcat:3ug7r4gsz5hn5fgnhxx7vxuqey
Graphical Models: Foundations of Neural Computation
2002
Pattern Analysis and Applications
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1 Bnl/c>sin~r ~ic'tulork, or-the graph may he undircctcci, in \~~I i i c h casc tlic' model is genesally referred to a s a Mur.ko71 rlll~dotri ficllj. ...
The selationship between these components underlies the computational machincq~ associated with graphical models. ...
or variational methods are generally required. ...
doi:10.1007/s100440200036
fatcat:bt75wlwba5hefifledkf62lv4e
An Explicit Discussion and Illustration of Gravity Anomaly & Bouguer Effects
2020
jecet
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Gravity method has tremendously penetrated widely the field of geophysical exploration being crucial and thus extensively discussed and vividly illustrated in this work.The gravity anomaly has been extensively ...
A computational extension can be done based on the symmetric matrix representation with available gravity data and a vivid illustration for anomaly and residual computation has been made elsewhere and ...
INTRODUCTION Gravity method has been extensively used and a widely explored tool in geophysical exploration for several years spanning over decades. ...
doi:10.24214/jecet.c.9.3.36266
fatcat:qushgdxkwzdjdib2xcffcvwowq
Cogeneration Improvement Based on Steam Cascade Analysis
2013
Chemical Engineering Transactions
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This graphical method would be helpful to scope the potential cogeneration improvement by variation of steam mains. The insights gained can be used to simplify the optimization of the utility system. ...
This work presents an extended graphical approach based on steam cascade analysis to explore how steam mains selection influences cogeneration improvement. ...
The graphical method can be used for conceptual design and optimization as a visualization tool to better understand the integration processes and utility systems. ...
doi:10.3303/cet1335002
doaj:b807f5bb993641bd832150d2e5a68411
fatcat:n4vxxkkmpnfwfdijnmoxvlsmti
Association between tobacco plain packaging and Quitline calls: a population-based, interrupted time-series analysis
2014
Medical Journal of Australia
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Thanks also to Bruce Armstrong, Sanchia Aranda, and Rebecca Kenyon for their helpful comments on the manuscript. ...
Acknowledgements: We thank Donna Perez and James Kite for help with obtaining the data. This study was internally funded by the Cancer Institute NSW. ...
of plain tobacco packaging and graphic health warnings The same modelling approach was used for fitting models to both data subsets. ...
doi:10.5694/mja13.11070
pmid:24438415
fatcat:hcxurw4mbbfs5hx2wysmcrrhbq
The problem of determining the threshold for statistical analysis by the POT method: Application to wave data on the Moroccan Atlantic coast
2021
E3S Web of Conferences
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In a study of extreme waves by the Peak Over Threshold (POT) method, the determination of the threshold of data censoring is an essential step. ...
The sensitivity study allowed us to confirm that the exponential model is the best probability distribution to describe wave data in two points on the Moroccan Atlantic coast for the wave data period from ...
In addition to the graphically determined thresholds, and for comparison purposes, we added an additional value u'= 5.00. ...
doi:10.1051/e3sconf/202131404002
fatcat:sletmlol65hnhbftenixzwm3se
Magnetohydrodynamic flow of linear visco-elastic fluid model above a shrinking/stretching sheet: A series solution
2017
Scientia Iranica. International Journal of Science and Technology
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Results are presented graphically and in tabulated forms to study the e ciency and accuracy of the homotopy perturbation method. ...
The governing Navier-Stokes equations of the ow are transformed to an ordinary di erential equation by a suitable similarity transformation and stream function. ...
The visco-elastic uid models are already used in simple models such as second-order model and/or Walters' B model, which are known to be good only for weakly elastic uids [1] subject to slow and/or slowly ...
doi:10.24200/sci.2017.4305
fatcat:7ws6mb3c7van3kzsufuasspweu
An Introduction to Variational Autoencoders
2019
Foundations and Trends® in Machine Learning
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In this work, we provide an introduction to variational autoencoders and some important extensions. ...
Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. ...
Acknowledgements We are grateful for the help of Tim Salimans, Alec Radford, Rif A. Saurous and others who have given us valuable feedback at various stages of writing. ...
doi:10.1561/2200000056
fatcat:t3x7k3dt65a5rlviyiixdnj3yi
Variational Learning in Graphical Models and Neural Networks
[chapter]
1998
ICANN 98
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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
Hierarchical Modeling for Phylogenetic Inference using RevBayes
[article]
2019
Figshare
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flexible model
specification
• graphical models
• easy and intuitive to
use Rev language
interface
Rev Language
n_branches <-2 * n_taxa -2
for(i in 1:n_branches){
branch_rates[i] ~ dnExp ...
• There is a clear need for
more flexible statical
software for phylogenetic
analysis
• Flexibility is needed for both
users and developers to
enable analysis under new
complex models
Challenges ...
doi:10.6084/m9.figshare.7886201.v1
fatcat:gzvvsnmkdfd5xgqu2bwunne4hy
Sequential variational inference for distributed multi-sensor tracking and fusion
2007
2007 10th International Conference on Information Fusion
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for graphical models, to infer the multi-sensor target states in time. ...
In particular, the sequential variational inference algorithm distributes the global inference to each node in graphical models. ...
VARIATIONAL METHODS FOR GRAPHICAL MODELS Variational methods are well-developed techniques for finding extremal functions. We briefly describe the use of variational methods for graphical models. ...
doi:10.1109/icif.2007.4408026
dblp:conf/fusion/DuP07
fatcat:pnd6g44eure5pnkjcnoe4mogoq
The Layout Generation Algorithm of Graphic Design Based on Transformer-CVAE
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2110.06794v1 fatcat:mnlqcu765bebnhjcsbr4baadem
It proposed an end-to-end graphic design layout generation model named LayoutT-CVAE. ...
This paper implemented the Transformer model and conditional variational autoencoder (CVAE) to the graphic design layout generation task. ...
LayoutT-CVAE model We propose a Transformer-based conditional variational autoencoder model called LayoutT-CVAE for the task of graphic design layout regression. ...
arXiv:2110.06794v2
fatcat:c5gweod5lrgdtg3qdsbrayuh6q
:{unav)
2012
Machine Learning
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This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). ...
We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. ...
Acknowledgments We wish to thank Brendan Frey, David Heckerman, Uffe Kjaerulff, and (as always) Peter Dayan for helpful comments on the manuscript.
Notes ...
doi:10.1023/a:1007665907178
fatcat:qrdodrap5fevncyagbxnuoylbq
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