'Inference problem' presentation slideshows

Integer Linear Programming in NLP Constrained Conditional Models

Integer Linear Programming in NLP Constrained Conditional Models. Ming-Wei Chang, Nick Rizzolo, Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign. June 2010 NAACL. Nice to Meet You.

1.64k views • 164 slides

Constrained Conditional Models Towards Better Semantic Analysis of Text

Constrained Conditional Models Towards Better Semantic Analysis of Text. Dan Roth Department of Computer Science University of Illinois at Urbana-Champaign. With thanks to: Collaborators: Ming-Wei Chang , Gourab Kundu, Lev Ratinov, Rajhans Samdani,

880 views • 75 slides

Image Matting and Its Applications

Image Matting and Its Applications. Chen-Yu Tseng Advisor: Sheng- Jyh Wang 2012-10-29. Image Matting. A process to extract foreground objects from an image, along with an alpha matte ( the opacity of the foreground color ). Input Image. Alpha Matte . Extracted Foreground.

918 views • 43 slides

CIAR Summer School Tutorial Lecture 1a: Mixtures of Gaussians, EM, and Variational Free Energy

CIAR Summer School Tutorial Lecture 1a: Mixtures of Gaussians, EM, and Variational Free Energy. Geoffrey Hinton. Stochastic generative model using directed acyclic graph (e.g. Bayes Net) Generation from model is easy Inference can be hard Learning is easy after inference.

548 views • 39 slides

Information Security Analytics

Information Security Analytics. Dr. Bhavani Thuraisingham The University of Texas at Dallas Multilevel Secure Data Management July 2012. Outline. What is an MLS/DBMS? Summary of Developments Challenges MLS/DBMS Designs and Prototypes Data Models and Functions Directions.

422 views • 33 slides

CS 440/ECE448 Lecture 20: Bayes Net Inference & Learning

CS 440/ECE448 Lecture 20: Bayes Net Inference & Learning. Slides by Svetlana Lazebnik , 11/2016 Modified by Mark Hasegawa-Johnson, 11/2017. Bayes Network Inference & Learning. Bayes net is a memory-efficient model of dependencies among: Query variables: X

684 views • 31 slides

Bayes network i nference

Bayes network i nference. A general scenario: Query variables: X Evidence ( observed ) variables and their values: E = e Unobserved variables: Y Inference problem : answer questions about the query variables given the evidence variables

278 views • 27 slides

Data and Applications Security Developments and Directions

Data and Applications Security Developments and Directions. Dr. Bhavani Thuraisingham The University of Texas at Dallas Inference Problem - II September 2012. Outline. Security Constraint Processing Use of Conceptual Structures. Security Constraint Processing.

543 views • 26 slides

Data and Applications Security Developments and Directions

Data and Applications Security Developments and Directions. Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #8 Inference Problem - II February 8, 2006. Outline. Security Constraint Processing Use of Conceptual Structures. Security Constraint Processing.

354 views • 26 slides

Data and Applications Security Developments and Directions

Data and Applications Security Developments and Directions. Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #12 Inference Problem - II September 24, 2010. Outline. Security Constraint Processing Use of Conceptual Structures. Security Constraint Processing.

267 views • 26 slides

Data and Applications Security Developments and Directions

Data and Applications Security Developments and Directions. Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #8 Inference Problem - II February 8, 2006. Outline. Security Constraint Processing Use of Conceptual Structures. Security Constraint Processing.

264 views • 26 slides

Building Trustworthy Semantic Webs

Building Trustworthy Semantic Webs. Dr. Bhavani Thuraisingham The University of Texas at Dallas Multilevel Secure Data Management and its implications to Multilevel semantic web technologies October 27, 2008. Outline. What is an MLS/DBMS? Summary of Developments Challenges Data Models

236 views • 23 slides

Chapter 6 Data Base Security

234 views • 23 slides

Data and Applications Security Developments and Directions

Data and Applications Security Developments and Directions. Guest Lecture Dr. Kevin Hamlen Given in February 2012. Objective of the Unit.

227 views • 22 slides

Feature-Enhanced Probabilistic Models for Diffusion Network Inference

Feature-Enhanced Probabilistic Models for Diffusion Network Inference. Stefano Ermon ECML-PKDD September 26, 2012 Joint work with Liaoruo Wang and John E. Hopcroft. Background. Diffusion processes common in many types of networks Cascading examples contact networks <> infections

397 views • 21 slides

Hidden Markov Model Cryptanalysis

358 views • 19 slides

GMCV 2014 International Workshop on Graphical Models in Computer Vision

GMCV 2014 International Workshop on Graphical Models in Computer Vision. 7 th September 2014, ECCV, Zurich Michael Ying Yang, University of Hannover Qinfeng (Javen) Shi, University of Adelaide Sebastian Nowozin, Microsoft Research. Graphical Models in Computer Vision.

281 views • 15 slides

Mean Field Variational Bayesian Data Assimilation EGU 2012, Vienna

Mean Field Variational Bayesian Data Assimilation EGU 2012, Vienna. Michail Vrettas 1 , Dan Cornford 1 , Manfred Opper 2 1 NCRG, Computer Science, Aston University, UK 2 Technical University of Berlin, Germany. Why do data assimilation?.

198 views • 11 slides

Jennifer Rexford Fall 2010 (TTh 1:30-2:50 in COS 302) COS 561: Advanced Computer Networks http://www.cs.princeton.edu/co

Network Diagnosis. Jennifer Rexford Fall 2010 (TTh 1:30-2:50 in COS 302) COS 561: Advanced Computer Networks http://www.cs.princeton.edu/courses/archive/fall10/cos561/. Networks Break (In Weird Ways). Bad things happen Reliability: link, router, firewall, DNS server, Web server

247 views • 7 slides