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en:projects:details:igm09 [2009/06/05 18:55]
masoud
en:projects:details:igm09 [2010/01/20 13:13]
masoud
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-===== Approximate inference in Graphical Models ===== +---- dataentry project ---- 
-\\+title :  ​Approximate inference in Graphical Models ​  
 +contactname:​ Masoud Alipour 
 +contactmail_mail:​ masoud.alipour@epfl.ch 
 +contacttel: 021 6937529 
 +contactroom:​ BC 150 
 +type : master semester  
 +status : completed 
 +created_dt : 2009-06-03 
 +taken_dt : 2009-06-15 
 +completed_dt : 2010-01-13 
 +by : Bogdan Stroe 
 +output_media :  
 +table : projects 
 +=====
 +template:​datatemplates:​project 
 +----
  
 Graphical Models are unifying framework for inference and learning for different applications such as Bioinformatics,​ speech processing, image processing, coding, and control theory. Many methods have been proposed for exact and approximate inference in graphical models. However, in most cases the exact inference is intractable and therefore the approximate inference methods are the focus of research in this area. One class of of approximate inference method is based on Linear and semidefinite programming relaxation of the problem. The aim of this project is to study and implement this method for general graphical models. Furthermore,​ the student should assess the performance,​ of this method on different datasets. The proportion of theoretical and empirical (programming,​ simulation) work in this project is 50-50 percent. ​ Graphical Models are unifying framework for inference and learning for different applications such as Bioinformatics,​ speech processing, image processing, coding, and control theory. Many methods have been proposed for exact and approximate inference in graphical models. However, in most cases the exact inference is intractable and therefore the approximate inference methods are the focus of research in this area. One class of of approximate inference method is based on Linear and semidefinite programming relaxation of the problem. The aim of this project is to study and implement this method for general graphical models. Furthermore,​ the student should assess the performance,​ of this method on different datasets. The proportion of theoretical and empirical (programming,​ simulation) work in this project is 50-50 percent. ​
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   * Semidefinite Programming (SDP)   * Semidefinite Programming (SDP)
   * Second-Order Cone Programming (SOCP)   * Second-Order Cone Programming (SOCP)
-  * Fast implementation of inference algorithms ​in graphical models+  * Fast implementation of inference algorithms ​for graphical models
   * Using and interfacing to existing efficient solvers   * Using and interfacing to existing efficient solvers
  
-Suggested Readings ​ +Suggested Readings ​and References ​
   * [[https://​www.nowpublishers.com/​product.aspx?​product=MAL&​doi=2200000001|Graphical Models, Exponential Families, and Variational Inference]],​ Martin J. Wainwright, ​ Michael I. Jordan  ​   * [[https://​www.nowpublishers.com/​product.aspx?​product=MAL&​doi=2200000001|Graphical Models, Exponential Families, and Variational Inference]],​ Martin J. Wainwright, ​ Michael I. Jordan  ​
   * [[http://​www.stanford.edu/​~boyd/​cvxbook/​|Convex Optimization]],​ Boyd, Vandenberghe   * [[http://​www.stanford.edu/​~boyd/​cvxbook/​|Convex Optimization]],​ Boyd, Vandenberghe
   * [[http://​research.microsoft.com/​en-us/​um/​people/​cmbishop/​prml/​|Pattern Recognition and Machine Learning]], Christopher M. Bishop ​   * [[http://​research.microsoft.com/​en-us/​um/​people/​cmbishop/​prml/​|Pattern Recognition and Machine Learning]], Christopher M. Bishop ​
- 
----- dataentry project ---- 
-title :  Approximate inference in Graphical Models  ​ 
-contactname:​ Masoud Alipour 
-contactmail_mail:​ masoud [dot] alipour [at] epfl [dot] ch 
-contacttel: 021 6937529 
-contactroom:​ BC 150 
-type : master semester ​ 
-status : available 
-created_dt : 2009-06-03 
-taken_dt :  ​ 
-completed_dt :  
-by :  
-output_media :  
-table : projects 
-====== 
-template:​datatemplates:​project 
-----